├── FlexModal_Protocols ├── CASIA-SURF_CeFA_test.txt ├── CASIA-SURF_CeFA_train.txt ├── CASIA-SURF_CeFA_val.txt ├── README.md ├── WMCA_test.txt ├── WMCA_test_fakehead.txt ├── WMCA_test_flexiblemask.txt ├── WMCA_test_glasses.txt ├── WMCA_test_papermask.txt ├── WMCA_test_print.txt ├── WMCA_test_replay.txt └── WMCA_test_rigidmask.txt ├── LICENSE ├── Load_FAS_MultiModal.py ├── Load_FAS_MultiModal_DropModal.py ├── Load_FAS_MultiModal_DropModal_test.py ├── README.md ├── models └── ViT_Base_CA.py ├── run.sh ├── train_FAS_ViT_AvgPool_CrossAtten_RGBDIR_P1234.py └── utils_FAS_MultiModal2.py /FlexModal_Protocols/README.md: -------------------------------------------------------------------------------- 1 | All the train/val/test splitings are the same as the original splitings in CASIA-SURF and CeFA datasets. 2 | We sample 3 frames for most videos. 3 | 4 | There are two small issues/sepcial settings in the protocols, and we mark here. 5 | 6 | -------------------------------- 7 | 8 | # For the CeFA dataset: 9 | We only sample the first frame of most of the videos in 'CeFA-Mask/3D-Mask' for testing as there are many frames missing in these directors. 10 | 11 | --------------------------------- 12 | 13 | # For the WMCA dataset: 14 | the names/index of two videos are not matched in three modality: 15 | 16 | WMCA-Image/R_CDIT/100_03_015_2_10/color/0012.jpg --> Depth and IR 's index not matched. 17 | 18 | WMCA-Image/R_CDIT/101_06_067_1_02/color/0020.jpg --> Depth and IR 's index not matched. 19 | -------------------------------------------------------------------------------- /FlexModal_Protocols/WMCA_test_fakehead.txt: -------------------------------------------------------------------------------- 1 | WMCA-Image/R_CDIT/501_01_000_2_02/color/0012.jpg WMCA-Image/R_CDIT/501_01_000_2_02/depth/0012.jpg WMCA-Image/R_CDIT/501_01_000_2_02/ir/0012.jpg 0 2 | WMCA-Image/R_CDIT/501_01_000_2_02/color/0024.jpg WMCA-Image/R_CDIT/501_01_000_2_02/depth/0024.jpg 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WMCA-Image/R_CDIT/533_06_008_1_14/depth/0026.jpg WMCA-Image/R_CDIT/533_06_008_1_14/ir/0026.jpg 0 402 | WMCA-Image/R_CDIT/533_06_008_1_14/color/0038.jpg WMCA-Image/R_CDIT/533_06_008_1_14/depth/0038.jpg WMCA-Image/R_CDIT/533_06_008_1_14/ir/0038.jpg 0 403 | WMCA-Image/R_CDIT/533_06_018_1_14/color/0012.jpg WMCA-Image/R_CDIT/533_06_018_1_14/depth/0012.jpg WMCA-Image/R_CDIT/533_06_018_1_14/ir/0012.jpg 0 404 | WMCA-Image/R_CDIT/533_06_018_1_14/color/0024.jpg WMCA-Image/R_CDIT/533_06_018_1_14/depth/0024.jpg WMCA-Image/R_CDIT/533_06_018_1_14/ir/0024.jpg 0 405 | WMCA-Image/R_CDIT/533_06_018_1_14/color/0036.jpg WMCA-Image/R_CDIT/533_06_018_1_14/depth/0036.jpg WMCA-Image/R_CDIT/533_06_018_1_14/ir/0036.jpg 0 406 | WMCA-Image/R_CDIT/533_07_019_1_14/color/0012.jpg WMCA-Image/R_CDIT/533_07_019_1_14/depth/0012.jpg WMCA-Image/R_CDIT/533_07_019_1_14/ir/0012.jpg 0 407 | WMCA-Image/R_CDIT/533_07_019_1_14/color/0024.jpg WMCA-Image/R_CDIT/533_07_019_1_14/depth/0024.jpg WMCA-Image/R_CDIT/533_07_019_1_14/ir/0024.jpg 0 408 | WMCA-Image/R_CDIT/533_07_019_1_14/color/0036.jpg WMCA-Image/R_CDIT/533_07_019_1_14/depth/0036.jpg WMCA-Image/R_CDIT/533_07_019_1_14/ir/0036.jpg 0 409 | WMCA-Image/R_CDIT/533_07_067_1_14/color/0012.jpg WMCA-Image/R_CDIT/533_07_067_1_14/depth/0012.jpg WMCA-Image/R_CDIT/533_07_067_1_14/ir/0012.jpg 0 410 | WMCA-Image/R_CDIT/533_07_067_1_14/color/0024.jpg WMCA-Image/R_CDIT/533_07_067_1_14/depth/0024.jpg WMCA-Image/R_CDIT/533_07_067_1_14/ir/0024.jpg 0 411 | WMCA-Image/R_CDIT/533_07_067_1_14/color/0036.jpg WMCA-Image/R_CDIT/533_07_067_1_14/depth/0036.jpg WMCA-Image/R_CDIT/533_07_067_1_14/ir/0036.jpg 0 412 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Zitong Yu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Load_FAS_MultiModal.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | import os 3 | import torch 4 | import pandas as pd 5 | #from skimage import io, transform 6 | import cv2 7 | import numpy as np 8 | import random 9 | import torch 10 | from torch.utils.data import Dataset, DataLoader 11 | from torchvision import transforms 12 | import pdb 13 | import math 14 | import os 15 | import imgaug.augmenters as iaa 16 | 17 | 18 | 19 | 20 | 21 | #face_scale = 0.9 #default for test, for training , can be set from [0.8 to 1.0] 22 | 23 | # data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5)) 24 | seq = iaa.Sequential([ 25 | iaa.Add(value=(-40,40), per_channel=True), # Add color 26 | iaa.GammaContrast(gamma=(0.5,1.5)) # GammaContrast with a gamma of 0.5 to 1.5 27 | ]) 28 | 29 | 30 | 31 | # Tensor 32 | class Cutout(object): 33 | def __init__(self, length=30): 34 | self.length = length 35 | 36 | def __call__(self, sample): 37 | img, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 38 | h, w = img.shape[1], img.shape[2] # Tensor [1][2], nparray [0][1] 39 | mask = np.ones((h, w), np.float32) 40 | y = np.random.randint(h) 41 | x = np.random.randint(w) 42 | length_new = np.random.randint(1, self.length) 43 | 44 | y1 = np.clip(y - length_new // 2, 0, h) 45 | y2 = np.clip(y + length_new // 2, 0, h) 46 | x1 = np.clip(x - length_new // 2, 0, w) 47 | x2 = np.clip(x + length_new // 2, 0, w) 48 | 49 | mask[y1: y2, x1: x2] = 0. 50 | mask = torch.from_numpy(mask) 51 | mask = mask.expand_as(img) 52 | img *= mask 53 | image_x_depth *= mask 54 | image_x_ir *= mask 55 | 56 | return {'image_x': img, 'image_x_depth': image_x_depth, 'image_x_ir': image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 57 | 58 | 59 | class Normaliztion(object): 60 | """ 61 | same as mxnet, normalize into [-1, 1] 62 | image = (image - 127.5)/128 63 | """ 64 | def __call__(self, sample): 65 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 66 | new_image_x = (image_x - 127.5)/128 # [-1,1] 67 | new_image_x_depth = (image_x_depth - 127.5)/128 # [-1,1] 68 | new_image_x_ir = (image_x_ir - 127.5)/128 # [-1,1] 69 | return {'image_x': new_image_x, 'image_x_depth': new_image_x_depth, 'image_x_ir': new_image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 70 | 71 | 72 | 73 | class RandomHorizontalFlip(object): 74 | """Horizontally flip the given Image randomly with a probability of 0.5.""" 75 | def __call__(self, sample): 76 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 77 | 78 | new_image_x = np.zeros((224, 224, 3)) 79 | new_image_x_depth = np.zeros((224, 224, 3)) 80 | new_image_x_ir = np.zeros((224, 224, 3)) 81 | 82 | p = random.random() 83 | if p < 0.5: 84 | #print('Flip') 85 | 86 | new_image_x = cv2.flip(image_x, 1) 87 | new_image_x_depth = cv2.flip(image_x_depth, 1) 88 | new_image_x_ir = cv2.flip(image_x_ir, 1) 89 | 90 | 91 | return {'image_x': new_image_x, 'image_x_depth': new_image_x_depth, 'image_x_ir': new_image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 92 | else: 93 | #print('no Flip') 94 | return {'image_x': image_x, 'image_x_depth': image_x_depth, 'image_x_ir': image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 95 | 96 | 97 | 98 | class ToTensor(object): 99 | """ 100 | Convert ndarrays in sample to Tensors. 101 | process only one batch every time 102 | """ 103 | 104 | def __call__(self, sample): 105 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 106 | 107 | # swap color axis because 108 | # numpy image: (batch_size) x H x W x C 109 | # torch image: (batch_size) x C X H X W 110 | image_x = image_x[:,:,::-1].transpose((2, 0, 1)) 111 | image_x = np.array(image_x) 112 | 113 | image_x_depth = image_x_depth[:,:,::-1].transpose((2, 0, 1)) 114 | image_x_depth = np.array(image_x_depth) 115 | 116 | image_x_ir = image_x_ir[:,:,::-1].transpose((2, 0, 1)) 117 | image_x_ir = np.array(image_x_ir) 118 | 119 | map_x1 = np.array(map_x1) 120 | 121 | spoofing_label_np = np.array([0],dtype=np.long) 122 | spoofing_label_np[0] = spoofing_label 123 | 124 | 125 | return {'image_x': torch.from_numpy(image_x.astype(np.float)).float(), 'image_x_depth': torch.from_numpy(image_x_depth.astype(np.float)).float(), 'image_x_ir': torch.from_numpy(image_x_ir.astype(np.float)).float(), 'spoofing_label': torch.from_numpy(spoofing_label_np.astype(np.long)).long(), 'map_x1': torch.from_numpy(map_x1.astype(np.float)).float()} 126 | 127 | 128 | # /home/ztyu/FAS_dataset/OULU/Train_images/ 6_3_20_5_121_scene.jpg 6_3_20_5_121_scene.dat 129 | # /home/ztyu/FAS_dataset/OULU/IJCB_re/OULUtrain_images/ 6_3_20_5_121_depth1D.jpg 130 | class Spoofing_train(Dataset): 131 | 132 | def __init__(self, info_list, root_dir, transform=None): 133 | 134 | self.landmarks_frame = pd.read_csv(info_list, delimiter=' ', header=None) 135 | self.root_dir = root_dir 136 | self.transform = transform 137 | 138 | def __len__(self): 139 | return len(self.landmarks_frame) 140 | 141 | 142 | def __getitem__(self, idx): 143 | #print(self.landmarks_frame.iloc[idx, 0]) 144 | videoname = str(self.landmarks_frame.iloc[idx, 0]) 145 | image_path = os.path.join(self.root_dir, videoname) 146 | 147 | videoname_depth = str(self.landmarks_frame.iloc[idx, 1]) 148 | image_path_depth = os.path.join(self.root_dir, videoname_depth) 149 | 150 | videoname_ir = str(self.landmarks_frame.iloc[idx, 2]) 151 | image_path_ir = os.path.join(self.root_dir, videoname_ir) 152 | 153 | 154 | #log_file2 = open('temp.txt', 'w') 155 | #log_file2.write('%s \n' % (image_path)) 156 | #log_file2.write('%s \n' % (image_path_depth)) 157 | #log_file2.write('%s \n' % (image_path_ir)) 158 | #log_file2.flush() 159 | 160 | image_x, map_x1 = self.get_single_image_x_RGB(image_path) 161 | image_x_depth = self.get_single_image_x(image_path_depth) 162 | image_x_ir = self.get_single_image_x(image_path_ir) 163 | 164 | spoofing_label = self.landmarks_frame.iloc[idx, 3] 165 | 166 | if spoofing_label == 1: # real 167 | spoofing_label = 1 # real 168 | #map_x1 = np.zeros((28, 28)) # real 169 | #map_x1 = np.ones((28, 28)) 170 | else: # fake 171 | spoofing_label = 0 172 | #map_x1 = np.ones((28, 28)) # fake 173 | map_x1 = np.zeros((28, 28)) 174 | 175 | 176 | sample = {'image_x': image_x, 'image_x_depth': image_x_depth, 'image_x_ir': image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 177 | 178 | if self.transform: 179 | sample = self.transform(sample) 180 | return sample 181 | 182 | def get_single_image_x_RGB(self, image_path): 183 | 184 | image_x = np.zeros((224, 224, 3)) 185 | binary_mask = np.zeros((28, 28)) 186 | 187 | # RGB 188 | image_x_temp = cv2.imread(image_path) 189 | 190 | #cv2.imwrite('temp.jpg', image_x_temp) 191 | 192 | image_x = cv2.resize(image_x_temp, (224, 224)) 193 | 194 | # data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5)) 195 | image_x_aug = seq.augment_image(image_x) 196 | 197 | image_x_temp_gray = cv2.imread(image_path, 0) 198 | image_x_temp_gray = cv2.resize(image_x_temp_gray, (28, 28)) 199 | for i in range(28): 200 | for j in range(28): 201 | if image_x_temp_gray[i,j]>0: 202 | binary_mask[i,j]=1 203 | else: 204 | binary_mask[i,j]=0 205 | 206 | return image_x_aug, binary_mask 207 | 208 | def get_single_image_x(self, image_path): 209 | 210 | image_x = np.zeros((224, 224, 3)) 211 | 212 | # RGB 213 | image_x_temp = cv2.imread(image_path) 214 | 215 | #cv2.imwrite('temp.jpg', image_x_temp) 216 | 217 | image_x = cv2.resize(image_x_temp, (224, 224)) 218 | 219 | # data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5)) 220 | image_x_aug = seq.augment_image(image_x) 221 | 222 | 223 | return image_x_aug 224 | 225 | 226 | 227 | 228 | -------------------------------------------------------------------------------- /Load_FAS_MultiModal_DropModal.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | import os 3 | import torch 4 | import pandas as pd 5 | #from skimage import io, transform 6 | import cv2 7 | import numpy as np 8 | import random 9 | import torch 10 | from torch.utils.data import Dataset, DataLoader 11 | from torchvision import transforms 12 | import pdb 13 | import math 14 | import os 15 | import imgaug.augmenters as iaa 16 | 17 | 18 | 19 | 20 | 21 | #face_scale = 0.9 #default for test, for training , can be set from [0.8 to 1.0] 22 | 23 | # data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5)) 24 | seq = iaa.Sequential([ 25 | iaa.Add(value=(-40,40), per_channel=True), # Add color 26 | iaa.GammaContrast(gamma=(0.5,1.5)) # GammaContrast with a gamma of 0.5 to 1.5 27 | ]) 28 | 29 | 30 | 31 | # Tensor 32 | class Cutout(object): 33 | def __init__(self, length=30): 34 | self.length = length 35 | 36 | def __call__(self, sample): 37 | img, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 38 | h, w = img.shape[1], img.shape[2] # Tensor [1][2], nparray [0][1] 39 | mask = np.ones((h, w), np.float32) 40 | y = np.random.randint(h) 41 | x = np.random.randint(w) 42 | length_new = np.random.randint(1, self.length) 43 | 44 | y1 = np.clip(y - length_new // 2, 0, h) 45 | y2 = np.clip(y + length_new // 2, 0, h) 46 | x1 = np.clip(x - length_new // 2, 0, w) 47 | x2 = np.clip(x + length_new // 2, 0, w) 48 | 49 | mask[y1: y2, x1: x2] = 0. 50 | mask = torch.from_numpy(mask) 51 | mask = mask.expand_as(img) 52 | img *= mask 53 | image_x_depth *= mask 54 | image_x_ir *= mask 55 | 56 | return {'image_x': img, 'image_x_depth': image_x_depth, 'image_x_ir': image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 57 | 58 | 59 | class Normaliztion(object): 60 | """ 61 | same as mxnet, normalize into [-1, 1] 62 | image = (image - 127.5)/128 63 | """ 64 | def __call__(self, sample): 65 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 66 | new_image_x = (image_x - 127.5)/128 # [-1,1] 67 | new_image_x_depth = (image_x_depth - 127.5)/128 # [-1,1] 68 | new_image_x_ir = (image_x_ir - 127.5)/128 # [-1,1] 69 | return {'image_x': new_image_x, 'image_x_depth': new_image_x_depth, 'image_x_ir': new_image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 70 | 71 | 72 | 73 | class RandomHorizontalFlip(object): 74 | """Horizontally flip the given Image randomly with a probability of 0.5.""" 75 | def __call__(self, sample): 76 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 77 | 78 | new_image_x = np.zeros((224, 224, 3)) 79 | new_image_x_depth = np.zeros((224, 224, 3)) 80 | new_image_x_ir = np.zeros((224, 224, 3)) 81 | 82 | p = random.random() 83 | if p < 0.5: 84 | #print('Flip') 85 | 86 | new_image_x = cv2.flip(image_x, 1) 87 | new_image_x_depth = cv2.flip(image_x_depth, 1) 88 | new_image_x_ir = cv2.flip(image_x_ir, 1) 89 | 90 | 91 | return {'image_x': new_image_x, 'image_x_depth': new_image_x_depth, 'image_x_ir': new_image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 92 | else: 93 | #print('no Flip') 94 | return {'image_x': image_x, 'image_x_depth': image_x_depth, 'image_x_ir': image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 95 | 96 | 97 | 98 | class ToTensor(object): 99 | """ 100 | Convert ndarrays in sample to Tensors. 101 | process only one batch every time 102 | """ 103 | 104 | def __call__(self, sample): 105 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 106 | 107 | # swap color axis because 108 | # numpy image: (batch_size) x H x W x C 109 | # torch image: (batch_size) x C X H X W 110 | image_x = image_x[:,:,::-1].transpose((2, 0, 1)) 111 | image_x = np.array(image_x) 112 | 113 | p = random.random() 114 | if p < 0.5: 115 | image_x_depth = image_x_depth[:,:,::-1].transpose((2, 0, 1)) 116 | image_x_depth = np.array(image_x_depth) 117 | else: 118 | # Blocked modality 119 | image_x_depth = np.zeros((3, 224, 224)) 120 | image_x_depth = np.array(image_x_depth) 121 | 122 | p1 = random.random() 123 | if p1 < 0.5: 124 | image_x_ir = image_x_ir[:,:,::-1].transpose((2, 0, 1)) 125 | image_x_ir = np.array(image_x_ir) 126 | else: 127 | # Blocked modality 128 | image_x_ir = np.zeros((3, 224, 224)) 129 | image_x_ir = np.array(image_x_ir) 130 | 131 | 132 | map_x1 = np.array(map_x1) 133 | 134 | spoofing_label_np = np.array([0],dtype=np.long) 135 | spoofing_label_np[0] = spoofing_label 136 | 137 | 138 | return {'image_x': torch.from_numpy(image_x.astype(np.float)).float(), 'image_x_depth': torch.from_numpy(image_x_depth.astype(np.float)).float(), 'image_x_ir': torch.from_numpy(image_x_ir.astype(np.float)).float(), 'spoofing_label': torch.from_numpy(spoofing_label_np.astype(np.long)).long(), 'map_x1': torch.from_numpy(map_x1.astype(np.float)).float()} 139 | 140 | 141 | # /home/ztyu/FAS_dataset/OULU/Train_images/ 6_3_20_5_121_scene.jpg 6_3_20_5_121_scene.dat 142 | # /home/ztyu/FAS_dataset/OULU/IJCB_re/OULUtrain_images/ 6_3_20_5_121_depth1D.jpg 143 | class Spoofing_train(Dataset): 144 | 145 | def __init__(self, info_list, root_dir, transform=None): 146 | 147 | self.landmarks_frame = pd.read_csv(info_list, delimiter=' ', header=None) 148 | self.root_dir = root_dir 149 | self.transform = transform 150 | 151 | def __len__(self): 152 | return len(self.landmarks_frame) 153 | 154 | 155 | def __getitem__(self, idx): 156 | #print(self.landmarks_frame.iloc[idx, 0]) 157 | videoname = str(self.landmarks_frame.iloc[idx, 0]) 158 | image_path = os.path.join(self.root_dir, videoname) 159 | 160 | videoname_depth = str(self.landmarks_frame.iloc[idx, 1]) 161 | image_path_depth = os.path.join(self.root_dir, videoname_depth) 162 | 163 | videoname_ir = str(self.landmarks_frame.iloc[idx, 2]) 164 | image_path_ir = os.path.join(self.root_dir, videoname_ir) 165 | 166 | 167 | #log_file2 = open('temp.txt', 'w') 168 | #log_file2.write('%s \n' % (image_path)) 169 | #log_file2.write('%s \n' % (image_path_depth)) 170 | #log_file2.write('%s \n' % (image_path_ir)) 171 | #log_file2.flush() 172 | 173 | image_x, map_x1 = self.get_single_image_x_RGB(image_path) 174 | image_x_depth = self.get_single_image_x(image_path_depth) 175 | image_x_ir = self.get_single_image_x(image_path_ir) 176 | 177 | spoofing_label = self.landmarks_frame.iloc[idx, 3] 178 | 179 | if spoofing_label == 1: # real 180 | spoofing_label = 1 # real 181 | #map_x1 = np.zeros((28, 28)) # real 182 | #map_x1 = np.ones((28, 28)) 183 | else: # fake 184 | spoofing_label = 0 185 | #map_x1 = np.ones((28, 28)) # fake 186 | map_x1 = np.zeros((28, 28)) 187 | 188 | 189 | sample = {'image_x': image_x, 'image_x_depth': image_x_depth, 'image_x_ir': image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 190 | 191 | if self.transform: 192 | sample = self.transform(sample) 193 | return sample 194 | 195 | def get_single_image_x_RGB(self, image_path): 196 | 197 | image_x = np.zeros((224, 224, 3)) 198 | binary_mask = np.zeros((28, 28)) 199 | 200 | # RGB 201 | image_x_temp = cv2.imread(image_path) 202 | 203 | #cv2.imwrite('temp.jpg', image_x_temp) 204 | 205 | image_x = cv2.resize(image_x_temp, (224, 224)) 206 | 207 | # data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5)) 208 | image_x_aug = seq.augment_image(image_x) 209 | 210 | image_x_temp_gray = cv2.imread(image_path, 0) 211 | image_x_temp_gray = cv2.resize(image_x_temp_gray, (28, 28)) 212 | for i in range(28): 213 | for j in range(28): 214 | if image_x_temp_gray[i,j]>0: 215 | binary_mask[i,j]=1 216 | else: 217 | binary_mask[i,j]=0 218 | 219 | return image_x_aug, binary_mask 220 | 221 | def get_single_image_x(self, image_path): 222 | 223 | image_x = np.zeros((224, 224, 3)) 224 | 225 | # RGB 226 | image_x_temp = cv2.imread(image_path) 227 | 228 | #cv2.imwrite('temp.jpg', image_x_temp) 229 | 230 | image_x = cv2.resize(image_x_temp, (224, 224)) 231 | 232 | # data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5)) 233 | image_x_aug = seq.augment_image(image_x) 234 | 235 | 236 | return image_x_aug 237 | 238 | 239 | 240 | 241 | -------------------------------------------------------------------------------- /Load_FAS_MultiModal_DropModal_test.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | import os 3 | import torch 4 | import pandas as pd 5 | #from skimage import io, transform 6 | import cv2 7 | import numpy as np 8 | import random 9 | import torch 10 | from torch.utils.data import Dataset, DataLoader 11 | from torchvision import transforms 12 | import pdb 13 | import math 14 | import os 15 | import imgaug.augmenters as iaa 16 | 17 | 18 | 19 | 20 | 21 | 22 | class Normaliztion_valtest(object): 23 | """ 24 | same as mxnet, normalize into [-1, 1] 25 | image = (image - 127.5)/128 26 | """ 27 | def __call__(self, sample): 28 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 29 | new_image_x = (image_x - 127.5)/128 # [-1,1] 30 | new_image_x_depth = (image_x_depth - 127.5)/128 # [-1,1] 31 | new_image_x_ir = (image_x_ir - 127.5)/128 # [-1,1] 32 | return {'image_x': new_image_x, 'image_x_depth': new_image_x_depth, 'image_x_ir': new_image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 33 | 34 | 35 | 36 | 37 | class ToTensor_valtest(object): 38 | """ 39 | Convert ndarrays in sample to Tensors. 40 | process only one batch every time 41 | """ 42 | 43 | def __call__(self, sample): 44 | image_x, image_x_depth, image_x_ir, spoofing_label, map_x1 = sample['image_x'],sample['image_x_depth'],sample['image_x_ir'],sample['spoofing_label'],sample['map_x1'] 45 | 46 | # swap color axis because 47 | # numpy image: (batch_size) x H x W x C 48 | # torch image: (batch_size) x C X H X W 49 | image_x = image_x[:,:,::-1].transpose((2, 0, 1)) 50 | image_x = np.array(image_x) 51 | 52 | image_x_depth = image_x_depth[:,:,::-1].transpose((2, 0, 1)) 53 | image_x_depth = np.array(image_x_depth) 54 | 55 | image_x_ir = image_x_ir[:,:,::-1].transpose((2, 0, 1)) 56 | image_x_ir = np.array(image_x) 57 | 58 | map_x1 = np.array(map_x1) 59 | 60 | spoofing_label_np = np.array([0],dtype=np.long) 61 | spoofing_label_np[0] = spoofing_label 62 | 63 | # Blocked modality 64 | image_x_zeros = np.zeros((3, 224, 224)) 65 | image_x_zeros = np.array(image_x_zeros) 66 | 67 | 68 | return {'image_x': torch.from_numpy(image_x.astype(np.float)).float(), 'image_x_depth': torch.from_numpy(image_x_depth.astype(np.float)).float(), 'image_x_ir': torch.from_numpy(image_x_ir.astype(np.float)).float(), 'spoofing_label': torch.from_numpy(spoofing_label_np.astype(np.long)).long(), 'map_x1': torch.from_numpy(map_x1.astype(np.float)).float(), 'image_x_zeros': torch.from_numpy(image_x_zeros.astype(np.float)).float()} 69 | 70 | 71 | 72 | class Spoofing_valtest(Dataset): 73 | 74 | def __init__(self, info_list, root_dir, transform=None): 75 | 76 | self.landmarks_frame = pd.read_csv(info_list, delimiter=' ', header=None) 77 | self.root_dir = root_dir 78 | self.transform = transform 79 | 80 | def __len__(self): 81 | return len(self.landmarks_frame) 82 | 83 | 84 | def __getitem__(self, idx): 85 | #print(self.landmarks_frame.iloc[idx, 0]) 86 | videoname = str(self.landmarks_frame.iloc[idx, 0]) 87 | image_path = os.path.join(self.root_dir, videoname) 88 | 89 | videoname_depth = str(self.landmarks_frame.iloc[idx, 1]) 90 | image_path_depth = os.path.join(self.root_dir, videoname_depth) 91 | 92 | videoname_ir = str(self.landmarks_frame.iloc[idx, 2]) 93 | image_path_ir = os.path.join(self.root_dir, videoname_ir) 94 | 95 | image_x, map_x1 = self.get_single_image_x_RGB(image_path) 96 | image_x_depth = self.get_single_image_x(image_path_depth) 97 | image_x_ir = self.get_single_image_x(image_path_ir) 98 | 99 | spoofing_label = self.landmarks_frame.iloc[idx, 3] 100 | 101 | if spoofing_label == 1: # real 102 | spoofing_label = 1 # real 103 | #map_x1 = np.zeros((28, 28)) # real 104 | #map_x1 = np.ones((28, 28)) 105 | else: # fake 106 | spoofing_label = 0 107 | #map_x1 = np.ones((28, 28)) # fake 108 | map_x1 = np.zeros((28, 28)) 109 | 110 | 111 | sample = {'image_x': image_x, 'image_x_depth': image_x_depth, 'image_x_ir': image_x_ir, 'spoofing_label': spoofing_label, 'map_x1': map_x1} 112 | 113 | if self.transform: 114 | sample = self.transform(sample) 115 | return sample 116 | 117 | def get_single_image_x_RGB(self, image_path): 118 | 119 | image_x = np.zeros((224, 224, 3)) 120 | binary_mask = np.zeros((28, 28)) 121 | 122 | # RGB 123 | image_x_temp = cv2.imread(image_path) 124 | 125 | image_x = cv2.resize(image_x_temp, (224, 224)) 126 | 127 | 128 | image_x_temp_gray = cv2.imread(image_path, 0) 129 | image_x_temp_gray = cv2.resize(image_x_temp_gray, (28, 28)) 130 | for i in range(28): 131 | for j in range(28): 132 | if image_x_temp_gray[i,j]>0: 133 | binary_mask[i,j]=1 134 | else: 135 | binary_mask[i,j]=0 136 | 137 | return image_x, binary_mask 138 | 139 | def get_single_image_x(self, image_path): 140 | 141 | image_x = np.zeros((224, 224, 3)) 142 | 143 | # RGB 144 | image_x_temp = cv2.imread(image_path) 145 | 146 | #cv2.imwrite('temp.jpg', image_x_temp) 147 | 148 | image_x = cv2.resize(image_x_temp, (224, 224)) 149 | 150 | 151 | return image_x 152 | 153 | 154 | 155 | 156 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Flexible-Modal FAS 2 | 3 | This is the official repository of "**[Flexible-Modal Face Anti-Spoofing: A Benchmark](https://arxiv.org/pdf/2202.08192.pdf)**" 4 | 5 | 6 | If you find our work useful in your research, please consider citing: 7 | 8 | @inproceedings{yu2023flexible, 9 | title={Flexible-Modal Face Anti-Spoofing: A Benchmark}, 10 | author={Yu, Zitong and Liu, Ajian and Zhao, Chenxu and Cheng, Kevin HM and Cheng, Xu and Zhao, Guoying}, 11 | booktitle={CVPRW}, 12 | year={2023} 13 | } 14 | 15 | -------------------------------------------------------------------------------- /models/ViT_Base_CA.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | import torchvision.models as models 4 | import torch 5 | 6 | 7 | import math 8 | 9 | import torch 10 | import torch.utils.model_zoo as model_zoo 11 | from torch.nn import Parameter 12 | import pdb 13 | import numpy as np 14 | import timm 15 | import timm.models.vision_transformer 16 | 17 | 18 | class VisionTransformer(timm.models.vision_transformer.VisionTransformer): 19 | 20 | def forward_features(self, x): 21 | x = self.patch_embed(x) 22 | cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks 23 | if self.dist_token is None: 24 | x = torch.cat((cls_token, x), dim=1) 25 | else: 26 | x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1) 27 | x = self.pos_drop(x + self.pos_embed) 28 | x = self.blocks(x) 29 | 30 | if self.dist_token is None: 31 | return x[:, 1:] 32 | else: 33 | return x[:, 2:] 34 | 35 | def forward(self, x): 36 | x = self.forward_features(x) 37 | return x 38 | 39 | 40 | timm.models.vision_transformer.VisionTransformer = VisionTransformer 41 | 42 | class ViT_AvgPool_3modal_CrossAtten_Channel(nn.Module): 43 | 44 | def __init__(self, pretrained=True): 45 | super(ViT_AvgPool_3modal_CrossAtten_Channel, self).__init__() 46 | self.vit = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=0) 47 | 48 | # binary CE 49 | self.fc = nn.Linear(768, 2) 50 | 51 | self.avgpool8 = nn.AdaptiveAvgPool2d((1, 1)) 52 | 53 | self.drop = nn.Dropout(0.3) 54 | self.drop2d = nn.Dropout2d(0.3) 55 | 56 | # fusion head 57 | self.ConvFuse = nn.Sequential( 58 | nn.Conv2d(768, 768, kernel_size=3, stride=1, padding=1), 59 | nn.BatchNorm2d(768), 60 | nn.ReLU(), 61 | ) 62 | 63 | def forward(self, x1, x2, x3): 64 | 65 | classtoken1 = self.vit.forward_features(x1) 66 | classtoken2 = self.vit.forward_features(x2) 67 | classtoken3 = self.vit.forward_features(x3) 68 | 69 | classtoken1 = classtoken1.transpose(1, 2).view(-1, 768, 14, 14) 70 | classtoken2 = classtoken2.transpose(1, 2).view(-1, 768, 14, 14) 71 | classtoken3 = classtoken3.transpose(1, 2).view(-1, 768, 14, 14) 72 | 73 | B,C,H,W = classtoken1.shape 74 | h1_temp = classtoken1.view(B,C,-1) 75 | h2_temp = classtoken2.view(B,C,-1) 76 | h3_temp = classtoken3.view(B,C,-1) 77 | 78 | crossh1_h2 = h2_temp @ h1_temp.transpose(-2, -1) # [64, 768, 768] 79 | #pdb.set_trace() 80 | crossh1_h2 =F.softmax(crossh1_h2, dim=-1) 81 | crossedh1_h2 = (crossh1_h2 @ h1_temp).contiguous() # [64, 768, 196] 82 | crossedh1_h2 = crossedh1_h2.view(B,C,H,W) 83 | 84 | crossh1_h3 = h3_temp @ h1_temp.transpose(-2, -1) 85 | crossh1_h3 =F.softmax(crossh1_h3, dim=-1) 86 | crossedh1_h3 = (crossh1_h3 @ h1_temp).contiguous() 87 | crossedh1_h3 = crossedh1_h3.view(B,C,H,W) 88 | 89 | #h_concat = torch.cat((classtoken1, crossedh1_h2, crossedh1_h3), dim=1) 90 | h_concat = classtoken1 + crossedh1_h2 + crossedh1_h3 91 | h_concat = self.ConvFuse(self.drop2d(h_concat)) 92 | 93 | regmap8 = self.avgpool8(h_concat) 94 | 95 | logits = self.fc(self.drop(regmap8.squeeze(-1).squeeze(-1))) 96 | 97 | return logits 98 | 99 | 100 | -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | module load pytorch/1.9 2 | pip install --user imgaug 3 | pip install --user timm 4 | pip install --user thop 5 | 6 | srun -N 1 -n 1 -c 2 -t 36:00:00 --gres=gpu:a100:1 -p gpusmall --account=project_2001654 python3 ~/ICIP_github/train_FAS_ViT_AvgPool_CrossAtten_RGBDIR_P1234.py 7 | 8 | #srun -N 1 -n 1 -c 2 -t 00:15:00 --gres=gpu:a100:1 -p gputest --account=project_2001654 python3 ~/ICIP_github/train_FAS_ViT_AvgPool_CrossAtten_RGBDIR_P1234.py 9 | 10 | -------------------------------------------------------------------------------- /utils_FAS_MultiModal2.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import torch 4 | import shutil 5 | import torchvision.transforms as transforms 6 | from torch.autograd import Variable 7 | import sklearn 8 | from sklearn import metrics 9 | from sklearn.metrics import roc_curve, auc 10 | import pandas as pd 11 | import pdb 12 | 13 | 14 | class AvgrageMeter(object): 15 | 16 | def __init__(self): 17 | self.reset() 18 | 19 | def reset(self): 20 | self.avg = 0 21 | self.sum = 0 22 | self.cnt = 0 23 | 24 | def update(self, val, n=1): 25 | self.sum += val * n 26 | self.cnt += n 27 | self.avg = self.sum / self.cnt 28 | 29 | 30 | 31 | 32 | def get_threshold(score_file): 33 | with open(score_file, 'r') as file: 34 | lines = file.readlines() 35 | 36 | data = [] 37 | count = 0.0 38 | num_real = 0.0 39 | num_fake = 0.0 40 | for line in lines: 41 | count += 1 42 | tokens = line.split() 43 | angle = float(tokens[0]) 44 | #pdb.set_trace() 45 | type = int(tokens[1]) 46 | data.append({'map_score': angle, 'label': type}) 47 | if type==1: 48 | num_real += 1 49 | else: 50 | num_fake += 1 51 | 52 | min_error = count # account ACER (or ACC) 53 | min_threshold = 0.0 54 | min_ACC = 0.0 55 | min_ACER = 0.0 56 | min_APCER = 0.0 57 | min_BPCER = 0.0 58 | 59 | 60 | for d in data: 61 | threshold = d['map_score'] 62 | 63 | type1 = len([s for s in data if s['map_score'] <= threshold and s['label'] == 1]) 64 | type2 = len([s for s in data if s['map_score'] > threshold and s['label'] == 0]) 65 | 66 | ACC = 1-(type1 + type2) / count 67 | APCER = type2 / num_fake 68 | BPCER = type1 / num_real 69 | ACER = (APCER + BPCER) / 2.0 70 | 71 | if ACER < min_error: 72 | min_error = ACER 73 | min_threshold = threshold 74 | min_ACC = ACC 75 | min_ACER = ACER 76 | min_APCER = APCER 77 | min_BPCER = min_BPCER 78 | 79 | # print(min_error, min_threshold) 80 | return min_threshold, min_ACC, min_APCER, min_BPCER, min_ACER 81 | 82 | 83 | 84 | def test_threshold_based(threshold, score_file): 85 | with open(score_file, 'r') as file: 86 | lines = file.readlines() 87 | 88 | data = [] 89 | count = 0.0 90 | num_real = 0.0 91 | num_fake = 0.0 92 | for line in lines: 93 | count += 1 94 | tokens = line.split() 95 | angle = float(tokens[0]) 96 | type = int(tokens[1]) 97 | data.append({'map_score': angle, 'label': type}) 98 | if type==1: 99 | num_real += 1 100 | else: 101 | num_fake += 1 102 | 103 | 104 | type1 = len([s for s in data if s['map_score'] <= threshold and s['label'] == 1]) 105 | type2 = len([s for s in data if s['map_score'] > threshold and s['label'] == 0]) 106 | 107 | ACC = 1-(type1 + type2) / count 108 | APCER = type2 / num_fake 109 | BPCER = type1 / num_real 110 | ACER = (APCER + BPCER) / 2.0 111 | 112 | return ACC, APCER, BPCER, ACER 113 | 114 | 115 | 116 | def get_err_threhold(fpr, tpr, threshold): 117 | RightIndex=(tpr+(1-fpr)-1); 118 | right_index = np.argmax(RightIndex) 119 | best_th = threshold[right_index] 120 | err = fpr[right_index] 121 | 122 | differ_tpr_fpr_1=tpr+fpr-1.0 123 | 124 | right_index = np.argmin(np.abs(differ_tpr_fpr_1)) 125 | best_th = threshold[right_index] 126 | err = fpr[right_index] 127 | 128 | #print(err, best_th) 129 | return err, best_th 130 | 131 | 132 | # TPR@FPR=0.001 133 | def tpr_fpr0001_funtion(y_true,y_predict): 134 | d = pd.DataFrame() 135 | d['prob'] = list(y_predict) 136 | d['y'] = list(y_true) 137 | d = d.sort_values(['prob'], ascending=[0]) 138 | y = d.y 139 | PosAll = pd.Series(y).value_counts()[1] 140 | NegAll = pd.Series(y).value_counts()[0] 141 | pCumsum = d['y'].cumsum() 142 | nCumsum = np.arange(len(y)) - pCumsum + 1 143 | pCumsumPer = pCumsum / PosAll 144 | nCumsumPer = nCumsum / NegAll 145 | TR1 = pCumsumPer[abs(nCumsumPer-0.001).idxmin()] 146 | #TR2 = pCumsumPer[abs(nCumsumPer-0.005).idxmin()] 147 | #TR3 = pCumsumPer[abs(nCumsumPer-0.01).idxmin()] 148 | return TR1 149 | 150 | 151 | # TPR@FPR=0.01 152 | def tpr_fpr001_funtion(y_true,y_predict): 153 | d = pd.DataFrame() 154 | d['prob'] = list(y_predict) 155 | d['y'] = list(y_true) 156 | d = d.sort_values(['prob'], ascending=[0]) 157 | y = d.y 158 | PosAll = pd.Series(y).value_counts()[1] 159 | NegAll = pd.Series(y).value_counts()[0] 160 | pCumsum = d['y'].cumsum() 161 | nCumsum = np.arange(len(y)) - pCumsum + 1 162 | pCumsumPer = pCumsum / PosAll 163 | nCumsumPer = nCumsum / NegAll 164 | TR1 = pCumsumPer[abs(nCumsumPer-0.01).idxmin()] 165 | #TR2 = pCumsumPer[abs(nCumsumPer-0.005).idxmin()] 166 | #TR3 = pCumsumPer[abs(nCumsumPer-0.01).idxmin()] 167 | return TR1 168 | 169 | 170 | 171 | 172 | def performances_FAS_MultiModal(CASIA_SURF_CeFA_val_filename, CASIA_SURF_CeFA_test_filename, WMCA_test_filename, WMCA_test_fakehead_filename, WMCA_test_flexiblemask_filename, WMCA_test_glasses_filename, WMCA_test_papermask_filename, WMCA_test_print_filename, WMCA_test_replay_filename, WMCA_test_rigidmask_filename): 173 | 174 | # val 175 | with open(CASIA_SURF_CeFA_val_filename, 'r') as file1: 176 | lines = file1.readlines() 177 | val_scores = [] 178 | val_labels = [] 179 | data = [] 180 | count = 0.0 181 | num_real = 0.0 182 | num_fake = 0.0 183 | for line in lines: 184 | count += 1 185 | tokens = line.split() 186 | score = float(tokens[0]) 187 | label = float(tokens[1]) #label = int(tokens[1]) 188 | val_scores.append(score) 189 | val_labels.append(label) 190 | data.append({'map_score': score, 'label': label}) 191 | if label==1: 192 | num_real += 1 193 | else: 194 | num_fake += 1 195 | 196 | fpr,tpr,threshold = roc_curve(val_labels, val_scores, pos_label=1) 197 | val_err, val_threshold = get_err_threhold(fpr, tpr, threshold) 198 | 199 | ################################################ 200 | # intra-test ACER_CASIA_SURF_CeFA 201 | with open(CASIA_SURF_CeFA_test_filename, 'r') as file2: 202 | lines = file2.readlines() 203 | test_scores = [] 204 | test_labels = [] 205 | data = [] 206 | count = 0.0 207 | num_real = 0.0 208 | num_fake = 0.0 209 | for line in lines: 210 | count += 1 211 | tokens = line.split() 212 | score = float(tokens[0]) 213 | label = float(tokens[1]) #label = int(tokens[1]) 214 | test_scores.append(score) 215 | test_labels.append(label) 216 | data.append({'map_score': score, 'label': label}) 217 | if label==1: 218 | num_real += 1 219 | else: 220 | num_fake += 1 221 | 222 | # test based on val_threshold 223 | type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1]) 224 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 225 | 226 | 227 | test_APCER = type2 / num_fake 228 | test_BPCER = type1 / num_real 229 | ACER_CASIA_SURF_CeFA = (test_APCER + test_BPCER) / 2.0 230 | 231 | 232 | 233 | # TPR_FPR0001 234 | TPR_FPR0001 = tpr_fpr0001_funtion(test_labels, test_scores) 235 | 236 | 237 | 238 | ################################################ 239 | # cross-test WMCA 240 | 241 | val_threshold = 0.5 242 | 243 | with open(WMCA_test_filename, 'r') as file3: 244 | lines = file3.readlines() 245 | test_scores = [] 246 | test_labels = [] 247 | data = [] 248 | count = 0.0 249 | num_real = 0.0 250 | num_fake = 0.0 251 | for line in lines: 252 | count += 1 253 | tokens = line.split() 254 | score = float(tokens[0]) 255 | label = float(tokens[1]) #label = int(tokens[1]) 256 | test_scores.append(score) 257 | test_labels.append(label) 258 | data.append({'map_score': score, 'label': label}) 259 | if label==1: 260 | num_real += 1 261 | else: 262 | num_fake += 1 263 | 264 | # test based on val_threshold 265 | type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1]) 266 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 267 | 268 | 269 | test_APCER = type2 / num_fake 270 | test_BPCER = type1 / num_real 271 | ACER_WMCA = (test_APCER + test_BPCER) / 2.0 272 | 273 | 274 | 275 | # TPR_FPR001 276 | TPR_FPR001 = tpr_fpr001_funtion(test_labels, test_scores) 277 | 278 | 279 | # cross-test fakehead 280 | with open(WMCA_test_fakehead_filename, 'r') as file5: 281 | lines = file5.readlines() 282 | test_scores = [] 283 | test_labels = [] 284 | data = [] 285 | count = 0.0 286 | num_real = 0.0 287 | num_fake = 0.0 288 | for line in lines: 289 | count += 1 290 | tokens = line.split() 291 | score = float(tokens[0]) 292 | label = float(tokens[1]) #label = int(tokens[1]) 293 | test_scores.append(score) 294 | test_labels.append(label) 295 | data.append({'map_score': score, 'label': label}) 296 | if label==1: 297 | num_real += 1 298 | else: 299 | num_fake += 1 300 | 301 | # test based on val_threshold 302 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 303 | APCER_fakehead = type2 / num_fake 304 | 305 | 306 | # cross-test flexiblemask 307 | with open(WMCA_test_flexiblemask_filename, 'r') as file6: 308 | lines = file6.readlines() 309 | test_scores = [] 310 | test_labels = [] 311 | data = [] 312 | count = 0.0 313 | num_real = 0.0 314 | num_fake = 0.0 315 | for line in lines: 316 | count += 1 317 | tokens = line.split() 318 | score = float(tokens[0]) 319 | label = float(tokens[1]) #label = int(tokens[1]) 320 | test_scores.append(score) 321 | test_labels.append(label) 322 | data.append({'map_score': score, 'label': label}) 323 | if label==1: 324 | num_real += 1 325 | else: 326 | num_fake += 1 327 | 328 | # test based on val_threshold 329 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 330 | APCER_flexiblemask = type2 / num_fake 331 | 332 | 333 | # cross-test glasses 334 | with open(WMCA_test_glasses_filename, 'r') as file7: 335 | lines = file7.readlines() 336 | test_scores = [] 337 | test_labels = [] 338 | data = [] 339 | count = 0.0 340 | num_real = 0.0 341 | num_fake = 0.0 342 | for line in lines: 343 | count += 1 344 | tokens = line.split() 345 | score = float(tokens[0]) 346 | label = float(tokens[1]) #label = int(tokens[1]) 347 | test_scores.append(score) 348 | test_labels.append(label) 349 | data.append({'map_score': score, 'label': label}) 350 | if label==1: 351 | num_real += 1 352 | else: 353 | num_fake += 1 354 | 355 | # test based on val_threshold 356 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 357 | APCER_glasses = type2 / num_fake 358 | 359 | 360 | # cross-test papermask 361 | with open(WMCA_test_papermask_filename, 'r') as file8: 362 | lines = file8.readlines() 363 | test_scores = [] 364 | test_labels = [] 365 | data = [] 366 | count = 0.0 367 | num_real = 0.0 368 | num_fake = 0.0 369 | for line in lines: 370 | count += 1 371 | tokens = line.split() 372 | score = float(tokens[0]) 373 | label = float(tokens[1]) #label = int(tokens[1]) 374 | test_scores.append(score) 375 | test_labels.append(label) 376 | data.append({'map_score': score, 'label': label}) 377 | if label==1: 378 | num_real += 1 379 | else: 380 | num_fake += 1 381 | 382 | # test based on val_threshold 383 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 384 | APCER_papermask = type2 / num_fake 385 | 386 | 387 | # cross-test print 388 | with open(WMCA_test_print_filename, 'r') as file9: 389 | lines = file9.readlines() 390 | test_scores = [] 391 | test_labels = [] 392 | data = [] 393 | count = 0.0 394 | num_real = 0.0 395 | num_fake = 0.0 396 | for line in lines: 397 | count += 1 398 | tokens = line.split() 399 | score = float(tokens[0]) 400 | label = float(tokens[1]) #label = int(tokens[1]) 401 | test_scores.append(score) 402 | test_labels.append(label) 403 | data.append({'map_score': score, 'label': label}) 404 | if label==1: 405 | num_real += 1 406 | else: 407 | num_fake += 1 408 | 409 | # test based on val_threshold 410 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 411 | APCER_print = type2 / num_fake 412 | 413 | 414 | 415 | # cross-test replay 416 | with open(WMCA_test_replay_filename, 'r') as file10: 417 | lines = file10.readlines() 418 | test_scores = [] 419 | test_labels = [] 420 | data = [] 421 | count = 0.0 422 | num_real = 0.0 423 | num_fake = 0.0 424 | for line in lines: 425 | count += 1 426 | tokens = line.split() 427 | score = float(tokens[0]) 428 | label = float(tokens[1]) #label = int(tokens[1]) 429 | test_scores.append(score) 430 | test_labels.append(label) 431 | data.append({'map_score': score, 'label': label}) 432 | if label==1: 433 | num_real += 1 434 | else: 435 | num_fake += 1 436 | 437 | # test based on val_threshold 438 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 439 | APCER_replay = type2 / num_fake 440 | 441 | 442 | 443 | # cross-test rigidmask 444 | with open(WMCA_test_rigidmask_filename, 'r') as file11: 445 | lines = file11.readlines() 446 | test_scores = [] 447 | test_labels = [] 448 | data = [] 449 | count = 0.0 450 | num_real = 0.0 451 | num_fake = 0.0 452 | for line in lines: 453 | count += 1 454 | tokens = line.split() 455 | score = float(tokens[0]) 456 | label = float(tokens[1]) #label = int(tokens[1]) 457 | test_scores.append(score) 458 | test_labels.append(label) 459 | data.append({'map_score': score, 'label': label}) 460 | if label==1: 461 | num_real += 1 462 | else: 463 | num_fake += 1 464 | 465 | # test based on val_threshold 466 | type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0]) 467 | APCER_rigidmask = type2 / num_fake 468 | 469 | 470 | 471 | return ACER_CASIA_SURF_CeFA, TPR_FPR0001, ACER_WMCA, test_APCER, test_BPCER, TPR_FPR001, APCER_fakehead, APCER_flexiblemask, APCER_glasses, APCER_papermask, APCER_print, APCER_replay, APCER_rigidmask 472 | 473 | 474 | 475 | --------------------------------------------------------------------------------