14 |
15 |
16 |
17 |
Frames Split
18 |
19 | {% for each_image in preprocessed_images %}
20 |
21 | {%endfor%}
22 |
23 |
24 |
25 |
Face Cropped Frames
26 |
27 | {% for each_image in faces_cropped_images %}
28 |
29 | {%endfor%}
30 |
31 |
32 |
33 |
Play to see Result
34 |
37 | {%if output == "REAL" %}
38 |
Result: {{output}}
39 | 
40 | {%else%}
41 | Result: {{output}}
42 | 
43 | {%endif%}
44 |
45 |
54 | {%endif%}
55 | {%endblock%}
56 | {%block js_cripts%}
57 |
58 |
110 | {%endblock%}
--------------------------------------------------------------------------------
/Django Application/ml_app/tests.py:
--------------------------------------------------------------------------------
1 | from django.test import TestCase
2 |
3 | # Create your tests here.
4 |
--------------------------------------------------------------------------------
/Django Application/ml_app/urls.py:
--------------------------------------------------------------------------------
1 | """project_settings URL Configuration
2 | """
3 | from django.contrib import admin
4 | from django.urls import path, include
5 | from . import views
6 | from .views import about, index, predict_page,cuda_full
7 |
8 | app_name = 'ml_app'
9 | handler404 = views.handler404
10 |
11 | urlpatterns = [
12 | path('', index, name='home'),
13 | path('about/', about, name='about'),
14 | path('predict/', predict_page, name='predict'),
15 | path('cuda_full/',cuda_full,name='cuda_full'),
16 | ]
17 |
--------------------------------------------------------------------------------
/Django Application/ml_app/views.py:
--------------------------------------------------------------------------------
1 | from django.shortcuts import render, redirect
2 | import torch
3 | import torchvision
4 | from torchvision import transforms, models
5 | from torch.utils.data import DataLoader
6 | from torch.utils.data.dataset import Dataset
7 | import os
8 | import numpy as np
9 | import cv2
10 | import matplotlib.pyplot as plt
11 | import face_recognition
12 | from torch.autograd import Variable
13 | import time
14 | import sys
15 | from torch import nn
16 | import json
17 | import glob
18 | import copy
19 | from torchvision import models
20 | import shutil
21 | from PIL import Image as pImage
22 | import time
23 | from django.conf import settings
24 | from .forms import VideoUploadForm
25 |
26 | index_template_name = 'index.html'
27 | predict_template_name = 'predict.html'
28 | about_template_name = "about.html"
29 |
30 | im_size = 112
31 | mean=[0.485, 0.456, 0.406]
32 | std=[0.229, 0.224, 0.225]
33 | sm = nn.Softmax()
34 | inv_normalize = transforms.Normalize(mean=-1*np.divide(mean,std),std=np.divide([1,1,1],std))
35 | if torch.cuda.is_available():
36 | device = 'gpu'
37 | else:
38 | device = 'cpu'
39 |
40 | train_transforms = transforms.Compose([
41 | transforms.ToPILImage(),
42 | transforms.Resize((im_size,im_size)),
43 | transforms.ToTensor(),
44 | transforms.Normalize(mean,std)])
45 |
46 | class Model(nn.Module):
47 |
48 | def __init__(self, num_classes,latent_dim= 2048, lstm_layers=1 , hidden_dim = 2048, bidirectional = False):
49 | super(Model, self).__init__()
50 | model = models.resnext50_32x4d(pretrained = True)
51 | self.model = nn.Sequential(*list(model.children())[:-2])
52 | self.lstm = nn.LSTM(latent_dim,hidden_dim, lstm_layers, bidirectional)
53 | self.relu = nn.LeakyReLU()
54 | self.dp = nn.Dropout(0.4)
55 | self.linear1 = nn.Linear(2048,num_classes)
56 | self.avgpool = nn.AdaptiveAvgPool2d(1)
57 |
58 | def forward(self, x):
59 | batch_size,seq_length, c, h, w = x.shape
60 | x = x.view(batch_size * seq_length, c, h, w)
61 | fmap = self.model(x)
62 | x = self.avgpool(fmap)
63 | x = x.view(batch_size,seq_length,2048)
64 | x_lstm,_ = self.lstm(x,None)
65 | return fmap,self.dp(self.linear1(x_lstm[:,-1,:]))
66 |
67 |
68 | class validation_dataset(Dataset):
69 | def __init__(self,video_names,sequence_length=60,transform = None):
70 | self.video_names = video_names
71 | self.transform = transform
72 | self.count = sequence_length
73 |
74 | def __len__(self):
75 | return len(self.video_names)
76 |
77 | def __getitem__(self,idx):
78 | video_path = self.video_names[idx]
79 | frames = []
80 | a = int(100/self.count)
81 | first_frame = np.random.randint(0,a)
82 | for i,frame in enumerate(self.frame_extract(video_path)):
83 | #if(i % a == first_frame):
84 | faces = face_recognition.face_locations(frame)
85 | try:
86 | top,right,bottom,left = faces[0]
87 | frame = frame[top:bottom,left:right,:]
88 | except:
89 | pass
90 | frames.append(self.transform(frame))
91 | if(len(frames) == self.count):
92 | break
93 | """
94 | for i,frame in enumerate(self.frame_extract(video_path)):
95 | if(i % a == first_frame):
96 | frames.append(self.transform(frame))
97 | """
98 | # if(len(frames)
1:
194 | accuracy = []
195 | for filename in sequence_model:
196 | acc = filename.split("_")[1]
197 | accuracy.append(acc) # Convert accuracy to float for proper comparison
198 | max_index = accuracy.index(max(accuracy))
199 | final_model = os.path.join(settings.PROJECT_DIR, "models", sequence_model[max_index])
200 | elif len(sequence_model) == 1:
201 | final_model = os.path.join(settings.PROJECT_DIR, "models", sequence_model[0])
202 | else:
203 | print("No model found for the specified sequence length.") # Handle no models found case
204 |
205 | return final_model
206 |
207 | ALLOWED_VIDEO_EXTENSIONS = set(['mp4','gif','webm','avi','3gp','wmv','flv','mkv'])
208 |
209 | def allowed_video_file(filename):
210 | #print("filename" ,filename.rsplit('.',1)[1].lower())
211 | if (filename.rsplit('.',1)[1].lower() in ALLOWED_VIDEO_EXTENSIONS):
212 | return True
213 | else:
214 | return False
215 | def index(request):
216 | if request.method == 'GET':
217 | video_upload_form = VideoUploadForm()
218 | if 'file_name' in request.session:
219 | del request.session['file_name']
220 | if 'preprocessed_images' in request.session:
221 | del request.session['preprocessed_images']
222 | if 'faces_cropped_images' in request.session:
223 | del request.session['faces_cropped_images']
224 | return render(request, index_template_name, {"form": video_upload_form})
225 | else:
226 | video_upload_form = VideoUploadForm(request.POST, request.FILES)
227 | if video_upload_form.is_valid():
228 | video_file = video_upload_form.cleaned_data['upload_video_file']
229 | video_file_ext = video_file.name.split('.')[-1]
230 | sequence_length = video_upload_form.cleaned_data['sequence_length']
231 | video_content_type = video_file.content_type.split('/')[0]
232 | if video_content_type in settings.CONTENT_TYPES:
233 | if video_file.size > int(settings.MAX_UPLOAD_SIZE):
234 | video_upload_form.add_error("upload_video_file", "Maximum file size 100 MB")
235 | return render(request, index_template_name, {"form": video_upload_form})
236 |
237 | if sequence_length <= 0:
238 | video_upload_form.add_error("sequence_length", "Sequence Length must be greater than 0")
239 | return render(request, index_template_name, {"form": video_upload_form})
240 |
241 | if allowed_video_file(video_file.name) == False:
242 | video_upload_form.add_error("upload_video_file","Only video files are allowed ")
243 | return render(request, index_template_name, {"form": video_upload_form})
244 |
245 | saved_video_file = 'uploaded_file_'+str(int(time.time()))+"."+video_file_ext
246 | if settings.DEBUG:
247 | with open(os.path.join(settings.PROJECT_DIR, 'uploaded_videos', saved_video_file), 'wb') as vFile:
248 | shutil.copyfileobj(video_file, vFile)
249 | request.session['file_name'] = os.path.join(settings.PROJECT_DIR, 'uploaded_videos', saved_video_file)
250 | else:
251 | with open(os.path.join(settings.PROJECT_DIR, 'uploaded_videos','app','uploaded_videos', saved_video_file), 'wb') as vFile:
252 | shutil.copyfileobj(video_file, vFile)
253 | request.session['file_name'] = os.path.join(settings.PROJECT_DIR, 'uploaded_videos','app','uploaded_videos', saved_video_file)
254 | request.session['sequence_length'] = sequence_length
255 | return redirect('ml_app:predict')
256 | else:
257 | return render(request, index_template_name, {"form": video_upload_form})
258 |
259 | def predict_page(request):
260 | if request.method == "GET":
261 | # Redirect to 'home' if 'file_name' is not in session
262 | if 'file_name' not in request.session:
263 | return redirect("ml_app:home")
264 | if 'file_name' in request.session:
265 | video_file = request.session['file_name']
266 | if 'sequence_length' in request.session:
267 | sequence_length = request.session['sequence_length']
268 | path_to_videos = [video_file]
269 | video_file_name = os.path.basename(video_file)
270 | video_file_name_only = os.path.splitext(video_file_name)[0]
271 | # Production environment adjustments
272 | if not settings.DEBUG:
273 | production_video_name = os.path.join('/home/app/staticfiles/', video_file_name.split('/')[3])
274 | print("Production file name", production_video_name)
275 | else:
276 | production_video_name = video_file_name
277 |
278 | # Load validation dataset
279 | video_dataset = validation_dataset(path_to_videos, sequence_length=sequence_length, transform=train_transforms)
280 |
281 | # Load model
282 | if(device == "gpu"):
283 | model = Model(2).cuda() # Adjust the model instantiation according to your model structure
284 | else:
285 | model = Model(2).cpu() # Adjust the model instantiation according to your model structure
286 | model_name = os.path.join(settings.PROJECT_DIR, 'models', get_accurate_model(sequence_length))
287 | path_to_model = os.path.join(settings.PROJECT_DIR, model_name)
288 | model.load_state_dict(torch.load(path_to_model, map_location=torch.device('cpu')))
289 | model.eval()
290 | start_time = time.time()
291 | # Display preprocessing images
292 | print("<=== | Started Videos Splitting | ===>")
293 | preprocessed_images = []
294 | faces_cropped_images = []
295 | cap = cv2.VideoCapture(video_file)
296 | frames = []
297 | while cap.isOpened():
298 | ret, frame = cap.read()
299 | if ret:
300 | frames.append(frame)
301 | else:
302 | break
303 | cap.release()
304 |
305 | print(f"Number of frames: {len(frames)}")
306 | # Process each frame for preprocessing and face cropping
307 | padding = 40
308 | faces_found = 0
309 | for i in range(sequence_length):
310 | if i >= len(frames):
311 | break
312 | frame = frames[i]
313 |
314 | # Convert BGR to RGB
315 | rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
316 |
317 | # Save preprocessed image
318 | image_name = f"{video_file_name_only}_preprocessed_{i+1}.png"
319 | image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', image_name)
320 | img_rgb = pImage.fromarray(rgb_frame, 'RGB')
321 | img_rgb.save(image_path)
322 | preprocessed_images.append(image_name)
323 |
324 | # Face detection and cropping
325 | face_locations = face_recognition.face_locations(rgb_frame)
326 | if len(face_locations) == 0:
327 | continue
328 |
329 | top, right, bottom, left = face_locations[0]
330 | frame_face = frame[top - padding:bottom + padding, left - padding:right + padding]
331 |
332 | # Convert cropped face image to RGB and save
333 | rgb_face = cv2.cvtColor(frame_face, cv2.COLOR_BGR2RGB)
334 | img_face_rgb = pImage.fromarray(rgb_face, 'RGB')
335 | image_name = f"{video_file_name_only}_cropped_faces_{i+1}.png"
336 | image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', image_name)
337 | img_face_rgb.save(image_path)
338 | faces_found += 1
339 | faces_cropped_images.append(image_name)
340 |
341 | print("<=== | Videos Splitting and Face Cropping Done | ===>")
342 | print("--- %s seconds ---" % (time.time() - start_time))
343 |
344 | # No face detected
345 | if faces_found == 0:
346 | return render(request, 'predict_template_name.html', {"no_faces": True})
347 |
348 | # Perform prediction
349 | try:
350 | heatmap_images = []
351 | output = ""
352 | confidence = 0.0
353 |
354 | for i in range(len(path_to_videos)):
355 | print("<=== | Started Prediction | ===>")
356 | prediction = predict(model, video_dataset[i], './', video_file_name_only)
357 | confidence = round(prediction[1], 1)
358 | output = "REAL" if prediction[0] == 1 else "FAKE"
359 | print("Prediction:", prediction[0], "==", output, "Confidence:", confidence)
360 | print("<=== | Prediction Done | ===>")
361 | print("--- %s seconds ---" % (time.time() - start_time))
362 |
363 | # Uncomment if you want to create heat map images
364 | # for j in range(sequence_length):
365 | # heatmap_images.append(plot_heat_map(j, model, video_dataset[i], './', video_file_name_only))
366 |
367 | # Render results
368 | context = {
369 | 'preprocessed_images': preprocessed_images,
370 | 'faces_cropped_images': faces_cropped_images,
371 | 'heatmap_images': heatmap_images,
372 | 'original_video': production_video_name,
373 | 'models_location': os.path.join(settings.PROJECT_DIR, 'models'),
374 | 'output': output,
375 | 'confidence': confidence
376 | }
377 |
378 | if settings.DEBUG:
379 | return render(request, predict_template_name, context)
380 | else:
381 | return render(request, predict_template_name, context)
382 |
383 | except Exception as e:
384 | print(f"Exception occurred during prediction: {e}")
385 | return render(request, 'cuda_full.html')
386 | def about(request):
387 | return render(request, about_template_name)
388 |
389 | def handler404(request,exception):
390 | return render(request, '404.html', status=404)
391 | def cuda_full(request):
392 | return render(request, 'cuda_full.html')
393 |
--------------------------------------------------------------------------------
/Django Application/nginx/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM nginx
2 | WORKDIR /etc/nginx/
3 | RUN rm /etc/nginx/conf.d/default.conf
4 | COPY nginx.conf /etc/nginx/conf.d
5 | EXPOSE 80
--------------------------------------------------------------------------------
/Django Application/nginx/nginx.conf:
--------------------------------------------------------------------------------
1 | upstream project_settings {
2 | server unix:/app/run/gunicorn.sock
3 | fail_timeout=0;
4 | }
5 |
6 | server {
7 |
8 | listen 80;
9 | # disable any limits to avoid HTTP 413 for large image uploads
10 | client_max_body_size 0;
11 |
12 | location / {
13 | if (!-f $request_filename) {
14 | proxy_pass http://project_settings;
15 | break;
16 | }
17 | proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
18 | proxy_set_header Host $host;
19 | proxy_redirect off;
20 | }
21 | #static file directory
22 | location /static/ {
23 | alias /home/app/staticfiles/;
24 | }
25 | #media file directory
26 | location /media/ {
27 | alias /app/uploaded_videos/;
28 | }
29 | }
30 |
31 |
32 |
--------------------------------------------------------------------------------
/Django Application/project_settings/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__init__.py
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/__init__.cpython-36.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/__init__.cpython-36.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/__init__.cpython-38.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/__init__.cpython-38.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/settings.cpython-36.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/settings.cpython-36.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/settings.cpython-38.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/settings.cpython-38.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/urls.cpython-36.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/urls.cpython-36.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/urls.cpython-38.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/urls.cpython-38.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/wsgi.cpython-36.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/wsgi.cpython-36.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/__pycache__/wsgi.cpython-38.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/project_settings/__pycache__/wsgi.cpython-38.pyc
--------------------------------------------------------------------------------
/Django Application/project_settings/asgi.py:
--------------------------------------------------------------------------------
1 | """
2 | ASGI config for project_settings project.
3 |
4 | It exposes the ASGI callable as a module-level variable named ``application``.
5 |
6 | For more information on this file, see
7 | https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/
8 | """
9 |
10 | import os
11 |
12 | from django.core.asgi import get_asgi_application
13 |
14 | os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project_settings.settings')
15 |
16 | application = get_asgi_application()
17 |
--------------------------------------------------------------------------------
/Django Application/project_settings/settings.py:
--------------------------------------------------------------------------------
1 | """
2 | Django settings for project_settings project.
3 | """
4 |
5 | import os
6 |
7 | # Build paths inside the project like this: os.path.join(BASE_DIR, ...)
8 | BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
9 |
10 | # Build paths inside the project like this: os.path.join(PROJECT_DIR, ...)
11 | PROJECT_DIR = os.path.abspath(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
12 |
13 |
14 | # Quick-start development settings - unsuitable for production
15 | # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/
16 |
17 | # SECURITY WARNING: keep the secret key used in production secret!
18 | SECRET_KEY = '@)0qp0!&-vht7k0wyuihr+nk-b8zrvb5j^1d@vl84cd1%)f=dz'
19 |
20 | # SECURITY WARNING: don't run with debug turned on in production!
21 | DEBUG = True
22 |
23 | # Change and set this to correct IP/Domain
24 | ALLOWED_HOSTS = ["*"]
25 |
26 |
27 | # Application definition
28 |
29 | INSTALLED_APPS = [
30 | 'django.contrib.contenttypes',
31 | 'django.contrib.sessions',
32 | 'django.contrib.messages',
33 | 'django.contrib.staticfiles',
34 | 'ml_app.apps.MlAppConfig'
35 | ]
36 |
37 | MIDDLEWARE = [
38 | 'django.middleware.security.SecurityMiddleware',
39 | 'django.contrib.sessions.middleware.SessionMiddleware',
40 | 'django.middleware.common.CommonMiddleware',
41 | 'django.middleware.csrf.CsrfViewMiddleware',
42 | 'django.contrib.messages.middleware.MessageMiddleware',
43 | 'django.middleware.clickjacking.XFrameOptionsMiddleware',
44 | ]
45 |
46 | ROOT_URLCONF = 'project_settings.urls'
47 |
48 | TEMPLATES = [
49 | {
50 | 'BACKEND': 'django.template.backends.django.DjangoTemplates',
51 | 'DIRS': [os.path.join(PROJECT_DIR, 'templates')],
52 | 'APP_DIRS': True,
53 | 'OPTIONS': {
54 | 'context_processors': [
55 | 'django.template.context_processors.debug',
56 | 'django.template.context_processors.request',
57 | 'django.contrib.messages.context_processors.messages',
58 | 'django.template.context_processors.media'
59 | ],
60 | },
61 | },
62 | ]
63 |
64 | WSGI_APPLICATION = 'project_settings.wsgi.application'
65 |
66 |
67 | # Database
68 | # https://docs.djangoproject.com/en/3.0/ref/settings/#databases
69 |
70 | DATABASES = {
71 | "default": {
72 | "ENGINE": "django.db.backends.sqlite3",
73 | "NAME": os.path.join(PROJECT_DIR, 'db.sqlite3'),
74 | }
75 | }
76 |
77 |
78 | # Internationalization
79 | # https://docs.djangoproject.com/en/3.0/topics/i18n/
80 |
81 | LANGUAGE_CODE = 'en-us'
82 |
83 | TIME_ZONE = 'UTC'
84 |
85 | USE_I18N = False
86 |
87 | USE_L10N = False
88 |
89 | USE_TZ = False
90 |
91 |
92 | # Static files (CSS, JavaScript, Images)
93 | # https://docs.djangoproject.com/en/3.0/howto/static-files/
94 |
95 | #used in production to serve static files
96 | STATIC_ROOT = "/home/app/staticfiles/"
97 |
98 | #url for static files
99 | STATIC_URL = '/static/'
100 |
101 | STATICFILES_DIRS = [
102 | os.path.join(PROJECT_DIR, 'uploaded_images'),
103 | os.path.join(PROJECT_DIR, 'static'),
104 | os.path.join(PROJECT_DIR, 'models'),
105 | ]
106 |
107 | CONTENT_TYPES = ['video']
108 | MAX_UPLOAD_SIZE = "104857600"
109 |
110 | MEDIA_URL = "/media/"
111 |
112 | MEDIA_ROOT = os.path.join(PROJECT_DIR, 'uploaded_videos')
113 |
114 | #for extra logging in production environment
115 | if DEBUG == False:
116 | LOGGING = {
117 | 'version': 1,
118 | 'disable_existing_loggers': False,
119 | 'handlers': {
120 | 'console': {
121 | 'class': 'logging.StreamHandler',
122 | },
123 | 'file': {
124 | 'level': 'DEBUG',
125 | 'class': 'logging.FileHandler',
126 | 'filename': 'log.django',
127 | },
128 | },
129 | 'loggers': {
130 | 'django': {
131 | 'handlers': ['console','file'],
132 | 'level': os.getenv('DJANGO_LOG_LEVEL', 'DEBUG'),
133 | },
134 | },
135 | }
136 |
--------------------------------------------------------------------------------
/Django Application/project_settings/urls.py:
--------------------------------------------------------------------------------
1 | """project_settings URL Configuration
2 | """
3 | from django.contrib import admin
4 | from django.urls import path, include
5 |
6 | from django.conf import settings
7 | from django.conf.urls.static import static
8 |
9 | urlpatterns = [
10 | path('', include('ml_app.urls')),
11 | ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
12 |
--------------------------------------------------------------------------------
/Django Application/project_settings/wsgi.py:
--------------------------------------------------------------------------------
1 | """
2 | WSGI config for project_settings project.
3 |
4 | It exposes the WSGI callable as a module-level variable named ``application``.
5 |
6 | For more information on this file, see
7 | https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/
8 | """
9 |
10 | import os
11 |
12 | from django.core.wsgi import get_wsgi_application
13 |
14 | os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project_settings.settings')
15 |
16 | application = get_wsgi_application()
17 |
--------------------------------------------------------------------------------
/Django Application/requirements.txt:
--------------------------------------------------------------------------------
1 | altair==5.3.0
2 | asgiref==3.8.1
3 | astor==0.8.1
4 | attrs==23.2.0
5 | backcall==0.2.0
6 | base58==2.1.1
7 | bleach==6.1.0
8 | blinker==1.8.2
9 | cachetools==5.3.3
10 | certifi==2024.6.2
11 | chardet==5.2.0
12 | click==8.1.7
13 | cmake==3.29.6
14 | colorama==0.4.6
15 | cycler==0.12.1
16 | decorator==5.1.1
17 | defusedxml==0.7.1
18 | Django==5.0.6
19 | dlib==19.24.2
20 | docutils==0.21.2
21 | entrypoints==0.4
22 | enum-compat==0.0.3
23 | face-recognition==1.3.0
24 | face-recognition-models==0.3.0
25 | future==1.0.0
26 | google==3.0.0
27 | google-api-core==2.19.1
28 | google-api-python-client==2.134.0
29 | google-auth==2.30.0
30 | google-auth-httplib2==0.2.0
31 | googleapis-common-protos==1.63.2
32 | httplib2==0.22.0
33 | idna==3.7
34 | ipykernel==6.29.4
35 | ipython==8.12.3
36 | ipython-genutils==0.2.0
37 | ipywidgets==8.1.3
38 | jedi==0.16.0
39 | Jinja2==3.1.4
40 | jmespath==1.0.1
41 | json5==0.9.25
42 | jsonschema==4.22.0
43 | jupyter-client==8.6.2
44 | jupyter-core==5.7.2
45 | jupyterlab==4.2.2
46 | jupyterlab-server==2.27.2
47 | kiwisolver==1.4.5
48 | MarkupSafe==2.1.5
49 | matplotlib==3.9.0
50 | mistune==3.0.2
51 | nbconvert==7.16.4
52 | nbformat==5.10.4
53 | notebook==7.2.1
54 | numpy===1.26.4
55 | opencv-python==4.10.0.84
56 | packaging==24.1
57 | pandas==2.2.2
58 | pandocfilters==1.5.1
59 | parso==0.8.4
60 | pathtools==0.1.2
61 | pickleshare==0.7.5
62 | Pillow==10.3.0
63 | prometheus-client==0.20.0
64 | prompt-toolkit==3.0.47
65 | protobuf==5.27.2
66 | pyasn1==0.6.0
67 | pyasn1-modules==0.4.0
68 | pycodestyle==2.12.0
69 | pydeck==0.9.0b1
70 | Pygments==2.18.0
71 | pyparsing==3.1.2
72 | pyrsistent==0.20.0
73 | python-dateutil==2.9.0
74 | # pytz==2019.3 Commenting for production deployment
75 | # pywin32==227
76 | pywinpty==0.5.7
77 | PyYAML==6.0.1
78 | pyzmq==26.0.3
79 | requests==2.32.3
80 | rsa==4.9
81 | s3transfer==0.10.2
82 | Send2Trash==1.8.3
83 | six==1.16.0
84 | soupsieve==2.5
85 | sqlparse==0.5.0
86 | terminado==0.18.1
87 | testpath==0.6.0
88 | toml==0.10.2
89 | toolz==0.12.1
90 | torch==2.3.1
91 | torchvision==0.18.1
92 | tornado==6.4.1
93 | traitlets==5.14.3
94 | tzlocal==5.2
95 | uritemplate==4.1.1
96 | urllib3==2.2.2
97 | validators==0.28.3
98 | watchdog==4.0.1
99 | wcwidth==0.2.13
100 | webencodings==0.5.1
101 | widgetsnbextension==4.0.11
102 |
--------------------------------------------------------------------------------
/Django Application/static/css/styles.css:
--------------------------------------------------------------------------------
1 | body {
2 | height: 100%;
3 | }
4 | .bg {
5 | /* The image used */
6 | background-image: url("/static/images/background1.png");
7 |
8 | -webkit-background-size: cover;
9 | -moz-background-size: cover;
10 | -o-background-size: cover;
11 |
12 | /* Center and scale the image nicely */
13 | background-position: center;
14 | background-repeat: no-repeat;
15 | background-size: cover;
16 |
17 |
18 | }
19 |
20 | section{
21 | padding-top: 60px;
22 | }
23 |
24 | .width-300{
25 | width: 300px;
26 | margin: auto;
27 | padding: 20px;
28 | box-shadow: 0 0px 9px 2px #ccc;
29 | }
30 |
31 | .width-400{
32 | width: 400px;
33 | margin: auto;
34 | padding: 20px;
35 | margin-top: 80px;
36 | margin-bottom: 150px;
37 | box-shadow: 0 0px 9px 2px #ccc;
38 | }
39 |
40 | .width-500{
41 | width: 500px;
42 | margin: auto;
43 | padding: 20px;
44 | box-shadow: 0 0px 9px 2px #ccc;
45 | }
46 |
47 | #videos{
48 | display: none;
49 | }
50 |
51 | canvas {
52 | position: absolute;
53 | top: 0;
54 | left: 0;
55 | }
56 |
57 | .preprocess {
58 | padding-right: 20px;
59 | padding-bottom: 50px;
60 | }
61 |
62 | #preprocessed_images {
63 | white-space: nowrap;
64 | width: auto;
65 | height: 250px;
66 | padding: 20px;
67 | overflow-x: scroll;
68 | overflow-y: hidden;
69 | box-shadow: 0 0px 9px 2px #ccc;
70 | }
71 |
72 | #faces_images {
73 | white-space: nowrap;
74 | width: auto;
75 | height: 150px;
76 | padding: 20px;
77 | overflow-x: scroll;
78 | overflow-y: hidden;
79 | box-shadow: 0 0px 9px 2px #ccc;
80 | }
81 |
82 | .faces {
83 | padding-right: 20px;
84 | padding-bottom: 50px;
85 | }
86 |
87 | #heatmap_images{
88 | white-space: nowrap;
89 | width: auto;
90 | height: 200px;
91 | padding: 20px;
92 | overflow-x: scroll;
93 | overflow-y: hidden;
94 | box-shadow: 0 0px 9px 2px #ccc;
95 | margin-bottom: 20px;
96 | }
97 |
98 | .heat-map {
99 | padding-right: 20px;
100 | padding-bottom: 50px;
101 | }
102 |
103 | /* span.spinner-border {
104 | display: none;
105 | } */
--------------------------------------------------------------------------------
/Django Application/static/images/Thumbs.db:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/images/Thumbs.db
--------------------------------------------------------------------------------
/Django Application/static/images/background.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/images/background.png
--------------------------------------------------------------------------------
/Django Application/static/images/background1.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/images/background1.png
--------------------------------------------------------------------------------
/Django Application/static/images/logo1.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/images/logo1.png
--------------------------------------------------------------------------------
/Django Application/static/images/thumpdown.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/images/thumpdown.png
--------------------------------------------------------------------------------
/Django Application/static/images/thumpup.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/images/thumpup.png
--------------------------------------------------------------------------------
/Django Application/static/js/script.js:
--------------------------------------------------------------------------------
1 | $(document).on("change", "#id_upload_video_file", function(evt) {var $source = $('#video_source');$source[0].src = URL.createObjectURL(this.files[0]);$source.parent()[0].load();$('#videos').css("display", "block");$('#id_upload_video_file').css("display", "none");});$('form').on('submit', function(e){$('#videoUpload').prop("disabled", true);$('#videoUpload').html('Uploading Video Loading...');});
--------------------------------------------------------------------------------
/Django Application/static/json/age_gender_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/age_gender_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/age_gender_model-weights_manifest.json:
--------------------------------------------------------------------------------
1 | [{"weights":[{"name":"entry_flow/conv_in/filters","shape":[3,3,3,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005431825039433498,"min":-0.7441600304023892}},{"name":"entry_flow/conv_in/bias","shape":[32],"dtype":"float32"},{"name":"entry_flow/reduction_block_0/separable_conv0/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005691980614381678,"min":-0.6090419257388395}},{"name":"entry_flow/reduction_block_0/separable_conv0/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009089225881239947,"min":-1.1179747833925135}},{"name":"entry_flow/reduction_block_0/separable_conv0/bias","shape":[64],"dtype":"float32"},{"name":"entry_flow/reduction_block_0/separable_conv1/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00683894624897078,"min":-0.8138346036275228}},{"name":"entry_flow/reduction_block_0/separable_conv1/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011632566358528886,"min":-1.3028474321552352}},{"name":"entry_flow/reduction_block_0/separable_conv1/bias","shape":[64],"dtype":"float32"},{"name":"entry_flow/reduction_block_0/expansion_conv/filters","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010254812240600587,"min":-0.9229331016540528}},{"name":"entry_flow/reduction_block_0/expansion_conv/bias","shape":[64],"dtype":"float32"},{"name":"entry_flow/reduction_block_1/separable_conv0/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0052509616403018725,"min":-0.6406173201168285}},{"name":"entry_flow/reduction_block_1/separable_conv0/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010788509424994973,"min":-1.4564487723743214}},{"name":"entry_flow/reduction_block_1/separable_conv0/bias","shape":[128],"dtype":"float32"},{"name":"entry_flow/reduction_block_1/separable_conv1/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00553213918910307,"min":-0.7025816770160899}},{"name":"entry_flow/reduction_block_1/separable_conv1/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013602388606351965,"min":-1.6186842441558837}},{"name":"entry_flow/reduction_block_1/separable_conv1/bias","shape":[128],"dtype":"float32"},{"name":"entry_flow/reduction_block_1/expansion_conv/filters","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007571851038465313,"min":-1.158493208885193}},{"name":"entry_flow/reduction_block_1/expansion_conv/bias","shape":[128],"dtype":"float32"},{"name":"middle_flow/main_block_0/separable_conv0/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005766328409606335,"min":-0.6688940955143349}},{"name":"middle_flow/main_block_0/separable_conv0/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.012136116214826995,"min":-1.5776951079275094}},{"name":"middle_flow/main_block_0/separable_conv0/bias","shape":[128],"dtype":"float32"},{"name":"middle_flow/main_block_0/separable_conv1/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004314773222979377,"min":-0.5652352922102984}},{"name":"middle_flow/main_block_0/separable_conv1/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01107162026798024,"min":-1.2400214700137868}},{"name":"middle_flow/main_block_0/separable_conv1/bias","shape":[128],"dtype":"float32"},{"name":"middle_flow/main_block_0/separable_conv2/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0036451735917259667,"min":-0.4848080876995536}},{"name":"middle_flow/main_block_0/separable_conv2/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008791744942758598,"min":-1.134135097615859}},{"name":"middle_flow/main_block_0/separable_conv2/bias","shape":[128],"dtype":"float32"},{"name":"middle_flow/main_block_1/separable_conv0/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004915751896652521,"min":-0.6095532351849126}},{"name":"middle_flow/main_block_1/separable_conv0/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010868691463096469,"min":-1.3368490499608656}},{"name":"middle_flow/main_block_1/separable_conv0/bias","shape":[128],"dtype":"float32"},{"name":"middle_flow/main_block_1/separable_conv1/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005010117269029804,"min":-0.6012140722835765}},{"name":"middle_flow/main_block_1/separable_conv1/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010311148213405235,"min":-1.3816938605963016}},{"name":"middle_flow/main_block_1/separable_conv1/bias","shape":[128],"dtype":"float32"},{"name":"middle_flow/main_block_1/separable_conv2/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004911523706772748,"min":-0.7367285560159123}},{"name":"middle_flow/main_block_1/separable_conv2/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008976466047997568,"min":-1.2207993825276693}},{"name":"middle_flow/main_block_1/separable_conv2/bias","shape":[128],"dtype":"float32"},{"name":"exit_flow/reduction_block/separable_conv0/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005074804436926748,"min":-0.7104726211697447}},{"name":"exit_flow/reduction_block/separable_conv0/pointwise_filter","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011453078307357489,"min":-1.4545409450344011}},{"name":"exit_flow/reduction_block/separable_conv0/bias","shape":[256],"dtype":"float32"},{"name":"exit_flow/reduction_block/separable_conv1/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007741751390344957,"min":-1.1380374543807086}},{"name":"exit_flow/reduction_block/separable_conv1/pointwise_filter","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011347713189966538,"min":-1.497898141075583}},{"name":"exit_flow/reduction_block/separable_conv1/bias","shape":[256],"dtype":"float32"},{"name":"exit_flow/reduction_block/expansion_conv/filters","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006717281014311547,"min":-0.8329428457746318}},{"name":"exit_flow/reduction_block/expansion_conv/bias","shape":[256],"dtype":"float32"},{"name":"exit_flow/separable_conv/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0027201742518181892,"min":-0.3237007359663645}},{"name":"exit_flow/separable_conv/pointwise_filter","shape":[1,1,256,512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010076364348916447,"min":-1.330080094056971}},{"name":"exit_flow/separable_conv/bias","shape":[512],"dtype":"float32"},{"name":"fc/age/weights","shape":[512,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008674054987290326,"min":-1.2664120281443876}},{"name":"fc/age/bias","shape":[1],"dtype":"float32"},{"name":"fc/gender/weights","shape":[512,2],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0029948226377075793,"min":-0.34140978069866407}},{"name":"fc/gender/bias","shape":[2],"dtype":"float32"}],"paths":["age_gender_model-shard1"]}]
--------------------------------------------------------------------------------
/Django Application/static/json/face_expression_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/face_expression_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/face_expression_model-weights_manifest.json:
--------------------------------------------------------------------------------
1 | [{"weights":[{"name":"dense0/conv0/filters","shape":[3,3,3,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0057930146946626555,"min":-0.7125408074435067}},{"name":"dense0/conv0/bias","shape":[32],"dtype":"float32"},{"name":"dense0/conv1/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006473719839956246,"min":-0.6408982641556684}},{"name":"dense0/conv1/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010509579321917366,"min":-1.408283629136927}},{"name":"dense0/conv1/bias","shape":[32],"dtype":"float32"},{"name":"dense0/conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005666389652326995,"min":-0.7252978754978554}},{"name":"dense0/conv2/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010316079270605948,"min":-1.1760330368490781}},{"name":"dense0/conv2/bias","shape":[32],"dtype":"float32"},{"name":"dense0/conv3/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0063220320963392074,"min":-0.853474333005793}},{"name":"dense0/conv3/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010322785377502442,"min":-1.4658355236053466}},{"name":"dense0/conv3/bias","shape":[32],"dtype":"float32"},{"name":"dense1/conv0/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0042531527724920535,"min":-0.5741756242864272}},{"name":"dense1/conv0/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010653339647779278,"min":-1.1825207009035}},{"name":"dense1/conv0/bias","shape":[64],"dtype":"float32"},{"name":"dense1/conv1/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005166931012097527,"min":-0.6355325144879957}},{"name":"dense1/conv1/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011478300188101974,"min":-1.3888743227603388}},{"name":"dense1/conv1/bias","shape":[64],"dtype":"float32"},{"name":"dense1/conv2/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006144821410085641,"min":-0.8479853545918185}},{"name":"dense1/conv2/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010541967317169788,"min":-1.3809977185492421}},{"name":"dense1/conv2/bias","shape":[64],"dtype":"float32"},{"name":"dense1/conv3/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005769844849904378,"min":-0.686611537138621}},{"name":"dense1/conv3/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010939095534530341,"min":-1.2689350820055196}},{"name":"dense1/conv3/bias","shape":[64],"dtype":"float32"},{"name":"dense2/conv0/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0037769308277204924,"min":-0.40790852939381317}},{"name":"dense2/conv0/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01188667194516051,"min":-1.4382873053644218}},{"name":"dense2/conv0/bias","shape":[128],"dtype":"float32"},{"name":"dense2/conv1/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006497045825509464,"min":-0.8381189114907208}},{"name":"dense2/conv1/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011632198913424622,"min":-1.3377028750438316}},{"name":"dense2/conv1/bias","shape":[128],"dtype":"float32"},{"name":"dense2/conv2/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005947182225246056,"min":-0.7969224181829715}},{"name":"dense2/conv2/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011436844339557722,"min":-1.4524792311238306}},{"name":"dense2/conv2/bias","shape":[128],"dtype":"float32"},{"name":"dense2/conv3/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006665432686899222,"min":-0.8998334127313949}},{"name":"dense2/conv3/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01283421422920975,"min":-1.642779421338848}},{"name":"dense2/conv3/bias","shape":[128],"dtype":"float32"},{"name":"dense3/conv0/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004711699953266218,"min":-0.6737730933170692}},{"name":"dense3/conv0/pointwise_filter","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010955964817720302,"min":-1.3914075318504784}},{"name":"dense3/conv0/bias","shape":[256],"dtype":"float32"},{"name":"dense3/conv1/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00554193468654857,"min":-0.7149095745647656}},{"name":"dense3/conv1/pointwise_filter","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.016790372250126858,"min":-2.484975093018775}},{"name":"dense3/conv1/bias","shape":[256],"dtype":"float32"},{"name":"dense3/conv2/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006361540626077091,"min":-0.8142772001378676}},{"name":"dense3/conv2/pointwise_filter","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01777329678628959,"min":-1.7062364914838006}},{"name":"dense3/conv2/bias","shape":[256],"dtype":"float32"},{"name":"dense3/conv3/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006900275922289082,"min":-0.8625344902861353}},{"name":"dense3/conv3/pointwise_filter","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015449936717164282,"min":-1.9003422162112067}},{"name":"dense3/conv3/bias","shape":[256],"dtype":"float32"},{"name":"fc/weights","shape":[256,7],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004834276554631252,"min":-0.7203072066400565}},{"name":"fc/bias","shape":[7],"dtype":"float32"}],"paths":["face_expression_model-shard1"]}]
--------------------------------------------------------------------------------
/Django Application/static/json/face_landmark_68_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/face_landmark_68_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/face_landmark_68_model-weights_manifest.json:
--------------------------------------------------------------------------------
1 | [{"weights":[{"name":"dense0/conv0/filters","shape":[3,3,3,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004853619781194949,"min":-0.5872879935245888}},{"name":"dense0/conv0/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004396426443960153,"min":-0.7298067896973853}},{"name":"dense0/conv1/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00635151559231328,"min":-0.5589333721235686}},{"name":"dense0/conv1/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009354315552057004,"min":-1.2628325995276957}},{"name":"dense0/conv1/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0029380727048013726,"min":-0.5846764682554731}},{"name":"dense0/conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0049374802439820535,"min":-0.6171850304977566}},{"name":"dense0/conv2/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009941946758943446,"min":-1.3421628124573652}},{"name":"dense0/conv2/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0030300481062309416,"min":-0.5272283704841838}},{"name":"dense0/conv3/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005672684837790097,"min":-0.7431217137505026}},{"name":"dense0/conv3/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010712201455060173,"min":-1.5639814124387852}},{"name":"dense0/conv3/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0030966934035806097,"min":-0.3839899820439956}},{"name":"dense1/conv0/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0039155554537679636,"min":-0.48161332081345953}},{"name":"dense1/conv0/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01023082966898002,"min":-1.094698774580862}},{"name":"dense1/conv0/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0027264176630506327,"min":-0.3871513081531898}},{"name":"dense1/conv1/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004583378632863362,"min":-0.5454220573107401}},{"name":"dense1/conv1/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00915846403907327,"min":-1.117332612766939}},{"name":"dense1/conv1/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003091680419211294,"min":-0.5966943209077797}},{"name":"dense1/conv2/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005407439727409214,"min":-0.708374604290607}},{"name":"dense1/conv2/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00946493943532308,"min":-1.2399070660273235}},{"name":"dense1/conv2/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004409168514550901,"min":-0.9788354102303}},{"name":"dense1/conv3/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004478132958505668,"min":-0.6493292789833219}},{"name":"dense1/conv3/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011063695888893277,"min":-1.2501976354449402}},{"name":"dense1/conv3/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003909627596537272,"min":-0.6646366914113363}},{"name":"dense2/conv0/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003213915404151468,"min":-0.3374611174359041}},{"name":"dense2/conv0/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010917326048308728,"min":-1.4520043644250609}},{"name":"dense2/conv0/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002800439152063108,"min":-0.38085972468058266}},{"name":"dense2/conv1/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0050568851770139206,"min":-0.6927932692509071}},{"name":"dense2/conv1/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01074961213504567,"min":-1.3222022926106174}},{"name":"dense2/conv1/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0030654204242369708,"min":-0.5487102559384177}},{"name":"dense2/conv2/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00591809165244009,"min":-0.917304206128214}},{"name":"dense2/conv2/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01092823346455892,"min":-1.366029183069865}},{"name":"dense2/conv2/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002681120470458386,"min":-0.36463238398234055}},{"name":"dense2/conv3/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0048311497650894465,"min":-0.5797379718107336}},{"name":"dense2/conv3/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011227761062921263,"min":-1.4483811771168429}},{"name":"dense2/conv3/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0034643323982463162,"min":-0.3360402426298927}},{"name":"dense3/conv0/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003394978887894574,"min":-0.49227193874471326}},{"name":"dense3/conv0/pointwise_filter","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010051267287310432,"min":-1.2765109454884247}},{"name":"dense3/conv0/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003142924752889895,"min":-0.4588670139219247}},{"name":"dense3/conv1/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00448304671867221,"min":-0.5872791201460595}},{"name":"dense3/conv1/pointwise_filter","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.016063522357566685,"min":-2.3613377865623026}},{"name":"dense3/conv1/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00287135781026354,"min":-0.47664539650374765}},{"name":"dense3/conv2/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006002906724518421,"min":-0.7923836876364315}},{"name":"dense3/conv2/pointwise_filter","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.017087187019048954,"min":-1.6061955797906016}},{"name":"dense3/conv2/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003124481205846749,"min":-0.46242321846531886}},{"name":"dense3/conv3/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006576311588287353,"min":-1.0193282961845398}},{"name":"dense3/conv3/pointwise_filter","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015590153955945782,"min":-1.99553970636106}},{"name":"dense3/conv3/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004453541601405424,"min":-0.6546706154065973}},{"name":"fc/weights","shape":[256,136],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010417488509533453,"min":-1.500118345372817}},{"name":"fc/bias","shape":[136],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0025084222648658005,"min":0.07683877646923065}}],"paths":["face_landmark_68_model-shard1"]}]
--------------------------------------------------------------------------------
/Django Application/static/json/face_landmark_68_tiny_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/face_landmark_68_tiny_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/face_landmark_68_tiny_model-weights_manifest.json:
--------------------------------------------------------------------------------
1 | [{"weights":[{"name":"dense0/conv0/filters","shape":[3,3,3,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008194216092427571,"min":-0.9423348506291708}},{"name":"dense0/conv0/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006839508168837603,"min":-0.8412595047670252}},{"name":"dense0/conv1/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009194007106855804,"min":-1.2779669878529567}},{"name":"dense0/conv1/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0036026100317637128,"min":-0.3170296827952067}},{"name":"dense0/conv1/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.000740380117706224,"min":-0.06367269012273527}},{"name":"dense0/conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":1,"min":0}},{"name":"dense0/conv2/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":1,"min":0}},{"name":"dense0/conv2/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0037702228508743585,"min":-0.6220867703942692}},{"name":"dense1/conv0/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0033707996209462483,"min":-0.421349952618281}},{"name":"dense1/conv0/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.014611541991140328,"min":-1.8556658328748217}},{"name":"dense1/conv0/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002832523046755323,"min":-0.30307996600281956}},{"name":"dense1/conv1/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006593170586754294,"min":-0.6329443763284123}},{"name":"dense1/conv1/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.012215249211180444,"min":-1.6001976466646382}},{"name":"dense1/conv1/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002384825547536214,"min":-0.3028728445370992}},{"name":"dense1/conv2/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005859645441466687,"min":-0.7617539073906693}},{"name":"dense1/conv2/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013121426806730382,"min":-1.7845140457153321}},{"name":"dense1/conv2/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0032247188044529336,"min":-0.46435950784122243}},{"name":"dense2/conv0/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002659512618008782,"min":-0.32977956463308894}},{"name":"dense2/conv0/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015499923743453681,"min":-1.9839902391620712}},{"name":"dense2/conv0/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0032450980999890497,"min":-0.522460794098237}},{"name":"dense2/conv1/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005911862382701799,"min":-0.792189559282041}},{"name":"dense2/conv1/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.021025861478319356,"min":-2.2077154552235325}},{"name":"dense2/conv1/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00349616945958605,"min":-0.46149436866535865}},{"name":"dense2/conv2/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008104994250278847,"min":-1.013124281284856}},{"name":"dense2/conv2/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.029337059282789044,"min":-3.5791212325002633}},{"name":"dense2/conv2/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0038808938334969913,"min":-0.4230174278511721}},{"name":"fc/weights","shape":[128,136],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.014016061670639936,"min":-1.8921683255363912}},{"name":"fc/bias","shape":[136],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0029505149698724935,"min":0.088760145008564}}],"paths":["face_landmark_68_tiny_model-shard1"]}]
--------------------------------------------------------------------------------
/Django Application/static/json/face_recognition_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/face_recognition_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/face_recognition_model-shard2:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/face_recognition_model-shard2
--------------------------------------------------------------------------------
/Django Application/static/json/face_recognition_model-weights_manifest.json:
--------------------------------------------------------------------------------
1 | [{"weights":[{"name":"conv32_down/conv/filters","shape":[7,7,3,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0005260649557207145,"min":-0.07101876902229645}},{"name":"conv32_down/conv/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":8.471445956577858e-7,"min":-0.00014740315964445472}},{"name":"conv32_down/scale/weights","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.06814416062598135,"min":5.788674831390381}},{"name":"conv32_down/scale/biases","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008471635042452345,"min":-0.931879854669758}},{"name":"conv32_1/conv1/conv/filters","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0007328585666768691,"min":-0.0974701893680236}},{"name":"conv32_1/conv1/conv/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":1.5952091238361e-8,"min":-0.000001978059313556764}},{"name":"conv32_1/conv1/scale/weights","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.02146628510718252,"min":3.1103382110595703}},{"name":"conv32_1/conv1/scale/biases","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0194976619645661,"min":-2.3787147596770644}},{"name":"conv32_1/conv2/conv/filters","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0004114975824075587,"min":-0.05267169054816751}},{"name":"conv32_1/conv2/conv/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":4.600177166424806e-9,"min":-5.70421968636676e-7}},{"name":"conv32_1/conv2/scale/weights","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.03400764932819441,"min":2.1677730083465576}},{"name":"conv32_1/conv2/scale/biases","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010974494616190593,"min":-1.240117891629537}},{"name":"conv32_2/conv1/conv/filters","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0005358753251094444,"min":-0.0760942961655411}},{"name":"conv32_2/conv1/conv/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":5.9886454383719385e-9,"min":-7.366033889197485e-7}},{"name":"conv32_2/conv1/scale/weights","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.014633869657329485,"min":2.769575357437134}},{"name":"conv32_2/conv1/scale/biases","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.022131107367721257,"min":-2.5229462399202234}},{"name":"conv32_2/conv2/conv/filters","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00030145110452876373,"min":-0.03949009469326805}},{"name":"conv32_2/conv2/conv/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":6.8779549306497095e-9,"min":-9.010120959151119e-7}},{"name":"conv32_2/conv2/scale/weights","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.03929369870354148,"min":4.8010945320129395}},{"name":"conv32_2/conv2/scale/biases","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010553357180427103,"min":-1.2452961472903983}},{"name":"conv32_3/conv1/conv/filters","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0003133527642371608,"min":-0.040735859350830905}},{"name":"conv32_3/conv1/conv/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":4.1064200719547974e-9,"min":-3.0387508532465503e-7}},{"name":"conv32_3/conv1/scale/weights","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009252088210161994,"min":2.333256721496582}},{"name":"conv32_3/conv1/scale/biases","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007104101251153385,"min":-0.34810096130651585}},{"name":"conv32_3/conv2/conv/filters","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00029995629892629733,"min":-0.031195455088334923}},{"name":"conv32_3/conv2/conv/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":5.62726418316814e-9,"min":-6.921534945296811e-7}},{"name":"conv32_3/conv2/scale/weights","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0467432975769043,"min":5.362040996551514}},{"name":"conv32_3/conv2/scale/biases","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010314425300149357,"min":-1.268674311918371}},{"name":"conv64_down/conv1/conv/filters","shape":[3,3,32,64],"dtype":"float32"},{"name":"conv64_down/conv1/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":8.373908033218849e-10,"min":-1.172347124650639e-7}},{"name":"conv64_down/conv1/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0066875364266189875,"min":2.5088400840759277}},{"name":"conv64_down/conv1/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01691421620986041,"min":-2.0973628100226906}},{"name":"conv64_down/conv2/conv/filters","shape":[3,3,64,64],"dtype":"float32"},{"name":"conv64_down/conv2/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":2.3252014483766877e-9,"min":-2.673981665633191e-7}},{"name":"conv64_down/conv2/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.032557439804077146,"min":2.6351239681243896}},{"name":"conv64_down/conv2/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015429047509735706,"min":-1.5429047509735707}},{"name":"conv64_1/conv1/conv/filters","shape":[3,3,64,64],"dtype":"float32"},{"name":"conv64_1/conv1/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":1.1319172039756998e-9,"min":-1.4941307092479238e-7}},{"name":"conv64_1/conv1/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007802607031429515,"min":3.401733160018921}},{"name":"conv64_1/conv1/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01425027146058924,"min":-0.6982633015688727}},{"name":"conv64_1/conv2/conv/filters","shape":[3,3,64,64],"dtype":"float32"},{"name":"conv64_1/conv2/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":2.5635019893325435e-9,"min":-2.717312108692496e-7}},{"name":"conv64_1/conv2/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.04062801716374416,"min":3.542381525039673}},{"name":"conv64_1/conv2/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007973166306813557,"min":-0.7415044665336609}},{"name":"conv64_2/conv1/conv/filters","shape":[3,3,64,64],"dtype":"float32"},{"name":"conv64_2/conv1/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":1.2535732661062331e-9,"min":-1.8302169685151004e-7}},{"name":"conv64_2/conv1/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005631206549850164,"min":2.9051668643951416}},{"name":"conv64_2/conv1/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01859012585060269,"min":-2.3795361088771445}},{"name":"conv64_2/conv2/conv/filters","shape":[3,3,64,64],"dtype":"float32"},{"name":"conv64_2/conv2/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":2.486726369919351e-9,"min":-3.5311514452854786e-7}},{"name":"conv64_2/conv2/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.03740917467603497,"min":5.571568965911865}},{"name":"conv64_2/conv2/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006418555858088475,"min":-0.5263215803632549}},{"name":"conv64_3/conv1/conv/filters","shape":[3,3,64,64],"dtype":"float32"},{"name":"conv64_3/conv1/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":7.432564576875473e-10,"min":-8.47312361763804e-8}},{"name":"conv64_3/conv1/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006400122362024644,"min":2.268010377883911}},{"name":"conv64_3/conv1/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010945847922680425,"min":-1.3353934465670119}},{"name":"conv64_3/conv2/conv/filters","shape":[3,3,64,64],"dtype":"float32"},{"name":"conv64_3/conv2/conv/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":2.278228722014533e-9,"min":-3.212302498040492e-7}},{"name":"conv64_3/conv2/scale/weights","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.029840927498013366,"min":7.038398265838623}},{"name":"conv64_3/conv2/scale/biases","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.010651412197187834,"min":-1.161003929493474}},{"name":"conv128_down/conv1/conv/filters","shape":[3,3,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00020040544662989823,"min":-0.022245004575918704}},{"name":"conv128_down/conv1/conv/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":4.3550543563576545e-10,"min":-4.311503812794078e-8}},{"name":"conv128_down/conv1/scale/weights","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007448580685783835,"min":2.830846071243286}},{"name":"conv128_down/conv1/scale/biases","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01211262824488621,"min":-1.6957679542840696}},{"name":"conv128_down/conv2/conv/filters","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00022380277514457702,"min":-0.02484210804104805}},{"name":"conv128_down/conv2/conv/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":9.031058637304466e-10,"min":-1.1650065642122761e-7}},{"name":"conv128_down/conv2/scale/weights","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.027663578706629135,"min":3.1111555099487305}},{"name":"conv128_down/conv2/scale/biases","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008878476946961646,"min":-1.029903325847551}},{"name":"conv128_1/conv1/conv/filters","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00022380667574265425,"min":-0.032899581334170175}},{"name":"conv128_1/conv1/conv/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":4.4147297756478345e-10,"min":-5.253528433020923e-8}},{"name":"conv128_1/conv1/scale/weights","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013599334978589825,"min":3.634530782699585}},{"name":"conv128_1/conv1/scale/biases","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.014059314073300829,"min":-1.4059314073300828}},{"name":"conv128_1/conv2/conv/filters","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00021715293474057143,"min":-0.02909849325523657}},{"name":"conv128_1/conv2/conv/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":9.887046963276768e-10,"min":-1.1370104007768284e-7}},{"name":"conv128_1/conv2/scale/weights","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.029993299409454943,"min":3.630716562271118}},{"name":"conv128_1/conv2/scale/biases","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00782704236460667,"min":-0.7200878975438136}},{"name":"conv128_2/conv1/conv/filters","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00017718105923895743,"min":-0.022324813464108636}},{"name":"conv128_2/conv1/conv/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":3.567012027797675e-10,"min":-5.243507680862582e-8}},{"name":"conv128_2/conv1/scale/weights","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007940645778880399,"min":4.927767753601074}},{"name":"conv128_2/conv1/scale/biases","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015933452867994122,"min":-1.5614783810634238}},{"name":"conv128_2/conv2/conv/filters","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0001451439717236687,"min":-0.01712698866339291}},{"name":"conv128_2/conv2/conv/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":1.0383988570966347e-9,"min":-1.2356946399449953e-7}},{"name":"conv128_2/conv2/scale/weights","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.02892604528688917,"min":4.750600814819336}},{"name":"conv128_2/conv2/scale/biases","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00797275748907351,"min":-0.7414664464838364}},{"name":"conv256_down/conv1/conv/filters","shape":[3,3,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0002698827827093648,"min":-0.03994265184098599}},{"name":"conv256_down/conv1/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":5.036909834755123e-10,"min":-6.396875490139006e-8}},{"name":"conv256_down/conv1/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.014870181738161573,"min":4.269900798797607}},{"name":"conv256_down/conv1/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.022031106200872685,"min":-3.1063859743230484}},{"name":"conv256_down/conv2/conv/filters","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00046430734150549946,"min":-0.03946612402796745}},{"name":"conv256_down/conv2/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":6.693064577513153e-10,"min":-7.630093618364995e-8}},{"name":"conv256_down/conv2/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.03475512242784687,"min":3.608360528945923}},{"name":"conv256_down/conv2/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01290142021927179,"min":-1.1482263995151893}},{"name":"conv256_1/conv1/conv/filters","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00037147209924810076,"min":-0.04234781931428348}},{"name":"conv256_1/conv1/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":3.2105515457510146e-10,"min":-3.467395669411096e-8}},{"name":"conv256_1/conv1/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.043242172166412955,"min":5.28542947769165}},{"name":"conv256_1/conv1/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01643658619300992,"min":-1.3149268954407936}},{"name":"conv256_1/conv2/conv/filters","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0003289232651392619,"min":-0.041773254672686264}},{"name":"conv256_1/conv2/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":9.13591691187321e-10,"min":-1.2333487831028833e-7}},{"name":"conv256_1/conv2/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0573908618852204,"min":4.360693454742432}},{"name":"conv256_1/conv2/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0164216583850337,"min":-1.3958409627278647}},{"name":"conv256_2/conv1/conv/filters","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00010476927912118389,"min":-0.015610622589056398}},{"name":"conv256_2/conv1/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":2.418552539068639e-10,"min":-2.539480166022071e-8}},{"name":"conv256_2/conv1/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.06024209564807368,"min":6.598613739013672}},{"name":"conv256_2/conv1/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01578534350675695,"min":-1.1049740454729864}},{"name":"conv256_2/conv2/conv/filters","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00005543030908002573,"min":-0.007427661416723448}},{"name":"conv256_2/conv2/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":1.0822061852320308e-9,"min":-1.515088659324843e-7}},{"name":"conv256_2/conv2/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.04302893993901272,"min":2.2855491638183594}},{"name":"conv256_2/conv2/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006792667566561232,"min":-0.8083274404207865}},{"name":"conv256_down_out/conv1/conv/filters","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.000568966465253456,"min":-0.05632768006009214}},{"name":"conv256_down_out/conv1/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":4.5347887884881677e-10,"min":-6.530095855422961e-8}},{"name":"conv256_down_out/conv1/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.017565592597512638,"min":4.594101905822754}},{"name":"conv256_down_out/conv1/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.04850864223405427,"min":-6.306123490427055}},{"name":"conv256_down_out/conv2/conv/filters","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0003739110687199761,"min":-0.06954745878191555}},{"name":"conv256_down_out/conv2/conv/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":1.2668428328152895e-9,"min":-2.2549802424112154e-7}},{"name":"conv256_down_out/conv2/scale/weights","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.04351314469879749,"min":4.31956672668457}},{"name":"conv256_down_out/conv2/scale/biases","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.021499746921015722,"min":-1.2039858275768804}},{"name":"fc","shape":[256,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.000357687911566566,"min":-0.04578405268052045}}],"paths":["face_recognition_model-shard1","face_recognition_model-shard2"]}]
--------------------------------------------------------------------------------
/Django Application/static/json/mtcnn_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/mtcnn_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/mtcnn_model-weights_manifest.json:
--------------------------------------------------------------------------------
1 | [{"paths":["mtcnn_model-shard1"],"weights":[{"dtype":"float32","name":"pnet/conv1/weights","shape":[3,3,3,10]},{"dtype":"float32","name":"pnet/conv1/bias","shape":[10]},{"dtype":"float32","name":"pnet/prelu1_alpha","shape":[10]},{"dtype":"float32","name":"pnet/conv2/weights","shape":[3,3,10,16]},{"dtype":"float32","name":"pnet/conv2/bias","shape":[16]},{"dtype":"float32","name":"pnet/prelu2_alpha","shape":[16]},{"dtype":"float32","name":"pnet/conv3/weights","shape":[3,3,16,32]},{"dtype":"float32","name":"pnet/conv3/bias","shape":[32]},{"dtype":"float32","name":"pnet/prelu3_alpha","shape":[32]},{"dtype":"float32","name":"pnet/conv4_1/weights","shape":[1,1,32,2]},{"dtype":"float32","name":"pnet/conv4_1/bias","shape":[2]},{"dtype":"float32","name":"pnet/conv4_2/weights","shape":[1,1,32,4]},{"dtype":"float32","name":"pnet/conv4_2/bias","shape":[4]},{"dtype":"float32","name":"rnet/conv1/weights","shape":[3,3,3,28]},{"dtype":"float32","name":"rnet/conv1/bias","shape":[28]},{"dtype":"float32","name":"rnet/prelu1_alpha","shape":[28]},{"dtype":"float32","name":"rnet/conv2/weights","shape":[3,3,28,48]},{"dtype":"float32","name":"rnet/conv2/bias","shape":[48]},{"dtype":"float32","name":"rnet/prelu2_alpha","shape":[48]},{"dtype":"float32","name":"rnet/conv3/weights","shape":[2,2,48,64]},{"dtype":"float32","name":"rnet/conv3/bias","shape":[64]},{"dtype":"float32","name":"rnet/prelu3_alpha","shape":[64]},{"dtype":"float32","name":"rnet/fc1/weights","shape":[576,128]},{"dtype":"float32","name":"rnet/fc1/bias","shape":[128]},{"dtype":"float32","name":"rnet/prelu4_alpha","shape":[128]},{"dtype":"float32","name":"rnet/fc2_1/weights","shape":[128,2]},{"dtype":"float32","name":"rnet/fc2_1/bias","shape":[2]},{"dtype":"float32","name":"rnet/fc2_2/weights","shape":[128,4]},{"dtype":"float32","name":"rnet/fc2_2/bias","shape":[4]},{"dtype":"float32","name":"onet/conv1/weights","shape":[3,3,3,32]},{"dtype":"float32","name":"onet/conv1/bias","shape":[32]},{"dtype":"float32","name":"onet/prelu1_alpha","shape":[32]},{"dtype":"float32","name":"onet/conv2/weights","shape":[3,3,32,64]},{"dtype":"float32","name":"onet/conv2/bias","shape":[64]},{"dtype":"float32","name":"onet/prelu2_alpha","shape":[64]},{"dtype":"float32","name":"onet/conv3/weights","shape":[3,3,64,64]},{"dtype":"float32","name":"onet/conv3/bias","shape":[64]},{"dtype":"float32","name":"onet/prelu3_alpha","shape":[64]},{"dtype":"float32","name":"onet/conv4/weights","shape":[2,2,64,128]},{"dtype":"float32","name":"onet/conv4/bias","shape":[128]},{"dtype":"float32","name":"onet/prelu4_alpha","shape":[128]},{"dtype":"float32","name":"onet/fc1/weights","shape":[1152,256]},{"dtype":"float32","name":"onet/fc1/bias","shape":[256]},{"dtype":"float32","name":"onet/prelu5_alpha","shape":[256]},{"dtype":"float32","name":"onet/fc2_1/weights","shape":[256,2]},{"dtype":"float32","name":"onet/fc2_1/bias","shape":[2]},{"dtype":"float32","name":"onet/fc2_2/weights","shape":[256,4]},{"dtype":"float32","name":"onet/fc2_2/bias","shape":[4]},{"dtype":"float32","name":"onet/fc2_3/weights","shape":[256,10]},{"dtype":"float32","name":"onet/fc2_3/bias","shape":[10]}]}]
--------------------------------------------------------------------------------
/Django Application/static/json/ssd_mobilenetv1_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/ssd_mobilenetv1_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/ssd_mobilenetv1_model-shard2:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/ssd_mobilenetv1_model-shard2
--------------------------------------------------------------------------------
/Django Application/static/json/tiny_face_detector_model-shard1:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Django Application/static/json/tiny_face_detector_model-shard1
--------------------------------------------------------------------------------
/Django Application/static/json/tiny_face_detector_model-weights_manifest.json:
--------------------------------------------------------------------------------
1 | [{"weights":[{"name":"conv0/filters","shape":[3,3,3,16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009007044399485869,"min":-1.2069439495311063}},{"name":"conv0/bias","shape":[16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005263455241334205,"min":-0.9211046672334858}},{"name":"conv1/depthwise_filter","shape":[3,3,16,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004001977630690033,"min":-0.5042491814669441}},{"name":"conv1/pointwise_filter","shape":[1,1,16,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013836609615999109,"min":-1.411334180831909}},{"name":"conv1/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0015159862590771096,"min":-0.30926119685173037}},{"name":"conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002666276225856706,"min":-0.317286870876948}},{"name":"conv2/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015265831292844286,"min":-1.6792414422128714}},{"name":"conv2/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0020280554598453,"min":-0.37113414915168985}},{"name":"conv3/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006100742489683862,"min":-0.8907084034938438}},{"name":"conv3/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.016276211832083907,"min":-2.0508026908425725}},{"name":"conv3/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003394414279975143,"min":-0.7637432129944072}},{"name":"conv4/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006716050119961009,"min":-0.8059260143953211}},{"name":"conv4/pointwise_filter","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.021875603993733724,"min":-2.8875797271728514}},{"name":"conv4/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0041141652009066415,"min":-0.8187188749804216}},{"name":"conv5/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008423839597141042,"min":-0.9013508368940915}},{"name":"conv5/pointwise_filter","shape":[1,1,256,512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.030007277283014035,"min":-3.8709387695088107}},{"name":"conv5/bias","shape":[512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008402082966823203,"min":-1.4871686851277068}},{"name":"conv8/filters","shape":[1,1,512,25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.028336129469030042,"min":-4.675461362389957}},{"name":"conv8/bias","shape":[25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002268134028303857,"min":-0.41053225912299807}}],"paths":["tiny_face_detector_model-shard1"]}]
--------------------------------------------------------------------------------
/Django Application/templates/base.html:
--------------------------------------------------------------------------------
1 |
2 |
3 | {% load static %}
4 |
5 |
6 |
7 |
9 |
10 |
11 |
12 |
13 |
14 | Deepfake Detection
15 |
16 |
17 | {%include 'nav-bar.html'%}
18 | {%block content%}
19 | {%endblock%}
20 |
22 |
23 |
26 |
27 |
29 |
30 |
31 | {%block js_cripts%}
32 | {%endblock%}
33 | {%include 'footer.html'%}
34 |
35 |
36 |
--------------------------------------------------------------------------------
/Django Application/templates/footer.html:
--------------------------------------------------------------------------------
1 |
--------------------------------------------------------------------------------
/Django Application/templates/nav-bar.html:
--------------------------------------------------------------------------------
1 |
--------------------------------------------------------------------------------
/Django Application/uploaded_images/Readme.txt:
--------------------------------------------------------------------------------
1 | All the images will come here
2 |
--------------------------------------------------------------------------------
/Django Application/uploaded_videos/Readme.txt:
--------------------------------------------------------------------------------
1 | All the video will come here
2 |
--------------------------------------------------------------------------------
/Documentation/B1_poster .pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Documentation/B1_poster .pdf
--------------------------------------------------------------------------------
/Documentation/ESE presentation.pptx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Documentation/ESE presentation.pptx
--------------------------------------------------------------------------------
/Documentation/IJSRDV8I10860.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Documentation/IJSRDV8I10860.pdf
--------------------------------------------------------------------------------
/Documentation/Project Report.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/Documentation/Project Report.pdf
--------------------------------------------------------------------------------
/Documentation/README.md:
--------------------------------------------------------------------------------
1 | # Please find some of the refrence blogs on Medium
2 |
3 |
4 | - [Deepfake Detection using Deep Learning Code Walkthrough (Part 1)](https://abhijithjadhav.medium.com/deepfake-detection-using-deep-learning-code-walkthrough-part-1-b5e43d45e59c?source=rss-d84b6adcd7dc------2)
5 | - [Multiple Images in Single Container Docker setup for Nvidia Cuda](https://abhijithjadhav.medium.com/multiple-images-in-single-container-docker-setup-for-nvidia-cuda-2f7d5548c2b0?source=rss-d84b6adcd7dc------2)
6 | - [Dockerise deepfake detection Django application using Nvidia Cuda](https://abhijithjadhav.medium.com/dockerise-deepfake-detection-django-application-using-nvidia-cuda-40cdda3b6d38?source=rss-d84b6adcd7dc------2)
7 | - [Keep your mouse moving to avoid going away on Microsoft Teams](https://abhijithjadhav.medium.com/keep-your-mouse-moving-to-avoid-going-away-on-microsoft-teams-8a7801313990?source=rss-d84b6adcd7dc------2)
8 | - [Deepfake Video Detection Using Long Short-Term Memory](https://abhijithjadhav.medium.com/deepfake-video-detection-using-long-short-term-memory-df3674f83ecc?source=rss-d84b6adcd7dc------2)
9 |
10 | 🔖 [More Blogs](https://medium.com/@abhijithjadhav)
11 |
--------------------------------------------------------------------------------
/Model Creation/Helpers/Create_csv_from_glob.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "import glob\n",
10 | "import pandas as pd"
11 | ]
12 | },
13 | {
14 | "cell_type": "code",
15 | "execution_count": 30,
16 | "metadata": {},
17 | "outputs": [],
18 | "source": [
19 | "video_files = glob.glob(\"F:/College/BE/Final year project/Dataset/Celeb-DF-v2/YouTube-real/*.mp4\")"
20 | ]
21 | },
22 | {
23 | "cell_type": "code",
24 | "execution_count": 31,
25 | "metadata": {},
26 | "outputs": [],
27 | "source": [
28 | "names =[]\n",
29 | "for i in video_files:\n",
30 | " names.append(i.split(\"\\\\\")[-1])"
31 | ]
32 | },
33 | {
34 | "cell_type": "code",
35 | "execution_count": 32,
36 | "metadata": {},
37 | "outputs": [
38 | {
39 | "data": {
40 | "text/html": [
41 | "\n",
42 | "\n",
55 | "
\n",
56 | " \n",
57 | " \n",
58 | " | \n",
59 | " 0 | \n",
60 | "
\n",
61 | " \n",
62 | " \n",
63 | " \n",
64 | " | 0 | \n",
65 | " 00000.mp4 | \n",
66 | "
\n",
67 | " \n",
68 | " | 1 | \n",
69 | " 00001.mp4 | \n",
70 | "
\n",
71 | " \n",
72 | " | 2 | \n",
73 | " 00002.mp4 | \n",
74 | "
\n",
75 | " \n",
76 | " | 3 | \n",
77 | " 00003.mp4 | \n",
78 | "
\n",
79 | " \n",
80 | " | 4 | \n",
81 | " 00004.mp4 | \n",
82 | "
\n",
83 | " \n",
84 | " | 5 | \n",
85 | " 00005.mp4 | \n",
86 | "
\n",
87 | " \n",
88 | " | 6 | \n",
89 | " 00006.mp4 | \n",
90 | "
\n",
91 | " \n",
92 | " | 7 | \n",
93 | " 00007.mp4 | \n",
94 | "
\n",
95 | " \n",
96 | " | 8 | \n",
97 | " 00008.mp4 | \n",
98 | "
\n",
99 | " \n",
100 | " | 9 | \n",
101 | " 00009.mp4 | \n",
102 | "
\n",
103 | " \n",
104 | " | 10 | \n",
105 | " 00010.mp4 | \n",
106 | "
\n",
107 | " \n",
108 | " | 11 | \n",
109 | " 00011.mp4 | \n",
110 | "
\n",
111 | " \n",
112 | " | 12 | \n",
113 | " 00012.mp4 | \n",
114 | "
\n",
115 | " \n",
116 | " | 13 | \n",
117 | " 00013.mp4 | \n",
118 | "
\n",
119 | " \n",
120 | " | 14 | \n",
121 | " 00014.mp4 | \n",
122 | "
\n",
123 | " \n",
124 | " | 15 | \n",
125 | " 00015.mp4 | \n",
126 | "
\n",
127 | " \n",
128 | " | 16 | \n",
129 | " 00016.mp4 | \n",
130 | "
\n",
131 | " \n",
132 | " | 17 | \n",
133 | " 00017.mp4 | \n",
134 | "
\n",
135 | " \n",
136 | " | 18 | \n",
137 | " 00018.mp4 | \n",
138 | "
\n",
139 | " \n",
140 | " | 19 | \n",
141 | " 00019.mp4 | \n",
142 | "
\n",
143 | " \n",
144 | " | 20 | \n",
145 | " 00020.mp4 | \n",
146 | "
\n",
147 | " \n",
148 | " | 21 | \n",
149 | " 00021.mp4 | \n",
150 | "
\n",
151 | " \n",
152 | " | 22 | \n",
153 | " 00022.mp4 | \n",
154 | "
\n",
155 | " \n",
156 | " | 23 | \n",
157 | " 00023.mp4 | \n",
158 | "
\n",
159 | " \n",
160 | " | 24 | \n",
161 | " 00024.mp4 | \n",
162 | "
\n",
163 | " \n",
164 | " | 25 | \n",
165 | " 00025.mp4 | \n",
166 | "
\n",
167 | " \n",
168 | " | 26 | \n",
169 | " 00026.mp4 | \n",
170 | "
\n",
171 | " \n",
172 | " | 27 | \n",
173 | " 00027.mp4 | \n",
174 | "
\n",
175 | " \n",
176 | " | 28 | \n",
177 | " 00028.mp4 | \n",
178 | "
\n",
179 | " \n",
180 | " | 29 | \n",
181 | " 00029.mp4 | \n",
182 | "
\n",
183 | " \n",
184 | " | ... | \n",
185 | " ... | \n",
186 | "
\n",
187 | " \n",
188 | " | 270 | \n",
189 | " 00270.mp4 | \n",
190 | "
\n",
191 | " \n",
192 | " | 271 | \n",
193 | " 00271.mp4 | \n",
194 | "
\n",
195 | " \n",
196 | " | 272 | \n",
197 | " 00272.mp4 | \n",
198 | "
\n",
199 | " \n",
200 | " | 273 | \n",
201 | " 00273.mp4 | \n",
202 | "
\n",
203 | " \n",
204 | " | 274 | \n",
205 | " 00274.mp4 | \n",
206 | "
\n",
207 | " \n",
208 | " | 275 | \n",
209 | " 00275.mp4 | \n",
210 | "
\n",
211 | " \n",
212 | " | 276 | \n",
213 | " 00276.mp4 | \n",
214 | "
\n",
215 | " \n",
216 | " | 277 | \n",
217 | " 00277.mp4 | \n",
218 | "
\n",
219 | " \n",
220 | " | 278 | \n",
221 | " 00278.mp4 | \n",
222 | "
\n",
223 | " \n",
224 | " | 279 | \n",
225 | " 00279.mp4 | \n",
226 | "
\n",
227 | " \n",
228 | " | 280 | \n",
229 | " 00280.mp4 | \n",
230 | "
\n",
231 | " \n",
232 | " | 281 | \n",
233 | " 00281.mp4 | \n",
234 | "
\n",
235 | " \n",
236 | " | 282 | \n",
237 | " 00282.mp4 | \n",
238 | "
\n",
239 | " \n",
240 | " | 283 | \n",
241 | " 00283.mp4 | \n",
242 | "
\n",
243 | " \n",
244 | " | 284 | \n",
245 | " 00284.mp4 | \n",
246 | "
\n",
247 | " \n",
248 | " | 285 | \n",
249 | " 00285.mp4 | \n",
250 | "
\n",
251 | " \n",
252 | " | 286 | \n",
253 | " 00286.mp4 | \n",
254 | "
\n",
255 | " \n",
256 | " | 287 | \n",
257 | " 00287.mp4 | \n",
258 | "
\n",
259 | " \n",
260 | " | 288 | \n",
261 | " 00288.mp4 | \n",
262 | "
\n",
263 | " \n",
264 | " | 289 | \n",
265 | " 00289.mp4 | \n",
266 | "
\n",
267 | " \n",
268 | " | 290 | \n",
269 | " 00290.mp4 | \n",
270 | "
\n",
271 | " \n",
272 | " | 291 | \n",
273 | " 00291.mp4 | \n",
274 | "
\n",
275 | " \n",
276 | " | 292 | \n",
277 | " 00292.mp4 | \n",
278 | "
\n",
279 | " \n",
280 | " | 293 | \n",
281 | " 00293.mp4 | \n",
282 | "
\n",
283 | " \n",
284 | " | 294 | \n",
285 | " 00294.mp4 | \n",
286 | "
\n",
287 | " \n",
288 | " | 295 | \n",
289 | " 00295.mp4 | \n",
290 | "
\n",
291 | " \n",
292 | " | 296 | \n",
293 | " 00296.mp4 | \n",
294 | "
\n",
295 | " \n",
296 | " | 297 | \n",
297 | " 00297.mp4 | \n",
298 | "
\n",
299 | " \n",
300 | " | 298 | \n",
301 | " 00298.mp4 | \n",
302 | "
\n",
303 | " \n",
304 | " | 299 | \n",
305 | " 00299.mp4 | \n",
306 | "
\n",
307 | " \n",
308 | "
\n",
309 | "
300 rows × 1 columns
\n",
310 | "
4 |
5 |
6 |
7 | ## Latest Update
8 | #### We have dockerised the [Django Application](https://github.com/abhijitjadhav1998/Deepfake_detection_using_deep_learning/tree/master/Django%20Application) now you can spin up a container within seconds without worring about dependencies
9 |
10 |
11 | ## 1. Introduction
12 | This projects aims in detection of video deepfakes using deep learning techniques like ResNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained ResNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the [documentaion](https://github.com/abhijitjadhav1998/Deepfake_detection_using_deep_learning/tree/master/Documentation).
13 |
14 | You can also watch [this Youtube video](https://www.youtube.com/watch?v=_q16aJTXVRE) to get a better intuition about the project.
15 | You can watch [this playList](https://www.youtube.com/watch?v=quJ8Rv84oA0&list=PLNIj0dkfMA1FsD5xR4IEc8vdwr66_WExl) for step by step installation.
16 |
17 | You can read more about the project on 
18 |
19 |
20 | ## 2. Directory Structure
21 | For ease of understanding the project is structured in below format
22 | ```
23 | Deepfake_detection_using_deep_learning
24 | |
25 | |--- Django Application
26 | |--- Model Creation
27 | |--- Documentaion
28 | ```
29 | 1. Django Application
30 | - This directory consists of the django made application of our work. Where a user can upload the video and submit it to the model for prediction. The trained model performs the prediction and the result is displayed on the screen.
31 | 2. Model Creation
32 | - This directory consists of the step by step process of creating and training a deepfake detection model using our approach.
33 | 3. Documentation
34 | - This directory consists of all the documentation done during the project
35 |
36 | ## 3. System Architecture
37 |
38 | 
39 |
40 |
41 | ## 4. Demo
42 | ### You can watch the [youtube video](https://www.youtube.com/watch?v=_q16aJTXVRE&t=823s) for demo
43 |
44 |
45 | 
46 |
47 |
48 | ## 5. Our Results
49 |
50 | | Model Name | No of videos | No of Frames | Accuracy |
51 | |------------|--------------|--------------|----------|
52 | |model_84_acc_10_frames_final_data.pt |6000 |10 |84.21461|
53 | |model_87_acc_20_frames_final_data.pt | 6000 |20 |87.79160|
54 | |model_89_acc_40_frames_final_data.pt | 6000| 40 |89.34681|
55 | |model_90_acc_60_frames_final_data.pt | 6000| 60 |90.59097 |
56 | |model_91_acc_80_frames_final_data.pt | 6000 | 80 | 91.49818 |
57 | |model_93_acc_100_frames_final_data.pt| 6000 | 100 | 93.58794|
58 |
59 | ## 6. Contributors
60 |
61 |
62 |
63 |
64 |
72 |
73 |
74 |
75 |
76 |
77 |
78 |
79 |
80 |
81 |
82 |
83 |
84 |
85 | #### 6.1 Offline Contributors
86 | 1. Jay Patel
87 | 2. Hitendra Patil
88 | 3. Abhishek Patange
89 |
90 | ## 7. License
91 |
92 | [](https://www.gnu.org/licenses/gpl-3.0)
93 |
94 | ## 8. We welcome Open Source Contribution.
95 | ### Below are the some changes that can be applied to the project. New Ideas will be appreciated.
96 | - [ ] Deploying the applications in free cloud
97 | - [ ] Creating open source API for detection
98 | - [ ] Batch processing of entire video instead of processing first 'x' frames.
99 | - [ ] Optimizing the code for faster execution.
100 | #### Completed
101 | - [X] Dockerizing the app
102 | - [X] Enabling working of project on Non Cuda Computers. i.e on normal or AMD GPUs
103 |
104 | ## 9. Dont forget to Star⭐ this repo 😉 and
105 |
--------------------------------------------------------------------------------
/github_assets/System Architecture.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/github_assets/System Architecture.png
--------------------------------------------------------------------------------
/github_assets/fake gif.gif:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/github_assets/fake gif.gif
--------------------------------------------------------------------------------
/github_assets/fakegif.gif:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/abhijithjadhav/Deepfake_detection_using_deep_learning/534f0e9915754b3a21eb1a838925f60d90f2866c/github_assets/fakegif.gif
--------------------------------------------------------------------------------
38 |
39 | ## We deserve a Coffee ☕ 
