├── assets ├── architecture.png ├── intro_header.png ├── temporal_dataset.png ├── table-results-video-level.png ├── table-results-temporal-manual.png └── table-results-temporal-random.png ├── requirements.txt ├── .gitignore ├── temporal_dataset ├── temporal_manual_gt.csv ├── temporal_rand_one_segment_gt.csv └── temporal_rand_two_segments_gt.csv ├── README.md └── evaluate.py /assets/architecture.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rgb91/temporal-deepfake-segmentation/HEAD/assets/architecture.png -------------------------------------------------------------------------------- /assets/intro_header.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rgb91/temporal-deepfake-segmentation/HEAD/assets/intro_header.png -------------------------------------------------------------------------------- /assets/temporal_dataset.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rgb91/temporal-deepfake-segmentation/HEAD/assets/temporal_dataset.png -------------------------------------------------------------------------------- /assets/table-results-video-level.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rgb91/temporal-deepfake-segmentation/HEAD/assets/table-results-video-level.png -------------------------------------------------------------------------------- /assets/table-results-temporal-manual.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rgb91/temporal-deepfake-segmentation/HEAD/assets/table-results-temporal-manual.png -------------------------------------------------------------------------------- /assets/table-results-temporal-random.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rgb91/temporal-deepfake-segmentation/HEAD/assets/table-results-temporal-random.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | deepface==0.0.79 2 | keras==2.14.0 3 | matplotlib==3.5.3 4 | numpy==1.19.3 5 | opencv_python==4.1.0.25 6 | pandas==1.3.5 7 | scikit_learn==1.0.2 8 | tensorflow_gpu==2.4.0 9 | torch==2.1.0 10 | tqdm==4.65.0 11 | transformers==4.34.1 12 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .vscode/* 2 | !.vscode/settings.json 3 | !.vscode/tasks.json 4 | !.vscode/launch.json 5 | !.vscode/extensions.json 6 | !.vscode/*.code-snippets 7 | 8 | # Local History for Visual Studio Code 9 | .history/ 10 | 11 | # Built Visual Studio Code Extensions 12 | *.vsix 13 | 14 | # Byte-compiled / optimized / DLL files 15 | __pycache__/ 16 | *.py[cod] 17 | *$py.class 18 | 19 | # Jupyter Notebook 20 | .ipynb_checkpoints 21 | .virtual_documents/ 22 | 23 | .idea 24 | 25 | data/ 26 | saved_models/ 27 | run_shell_outputs/ 28 | _archived/ -------------------------------------------------------------------------------- /temporal_dataset/temporal_manual_gt.csv: -------------------------------------------------------------------------------- 1 | id,total_frames,fake_start,fake_end 2 | 002_006.mp4,693,120,275 3 | 004_982.mp4,309,115,269 4 | 005_010.mp4,385,172,364 5 | 007_132.mp4,505,172,378 6 | 016_209.mp4,678,127,295 7 | 024_073.mp4,306,111,264 8 | 042_084.mp4,338,129,298 9 | 045_889.mp4,350,128,292 10 | 053_095.mp4,417,159,367 11 | 057_070.mp4,431,132,315 12 | 066_062.mp4,435,176,387 13 | 085_124.mp4,329,122,281 14 | 088_060.mp4,665,295,627 15 | 089_065.mp4,751,158,379 16 | 092_098.mp4,460,121,270 17 | 100_077.mp4,423,163,374 18 | 101_096.mp4,1148,210,462 19 | 112_892.mp4,418,114,269 20 | 124_085.mp4,318,116,275 21 | 125_038.mp4,500,119,271 22 | 131_518.mp4,306,111,258 23 | 132_007.mp4,413,172,378 24 | 151_225.mp4,347,122,274 25 | 165_137.mp4,387,170,363 26 | 166_167.mp4,756,126,296 27 | 182_242.mp4,620,128,293 28 | 184_205.mp4,1254,134,287 29 | 196_310.mp4,426,143,330 30 | 198_106.mp4,392,130,308 31 | 201_203.mp4,863,156,379 32 | 204_230.mp4,515,148,334 33 | 209_016.mp4,337,124,292 34 | 225_151.mp4,304,114,266 35 | 238_282.mp4,622,211,508 36 | 239_218.mp4,588,134,310 37 | 271_264.mp4,490,190,417 38 | 286_267.mp4,668,114,267 39 | 297_270.mp4,390,123,278 40 | 339_392.mp4,457,162,390 41 | 344_020.mp4,631,147,342 42 | 348_202.mp4,414,144,305 43 | 351_346.mp4,684,254,561 44 | 387_311.mp4,528,150,334 45 | 392_339.mp4,1496,182,410 46 | 393_405.mp4,579,202,467 47 | 397_602.mp4,355,147,324 48 | 412_274.mp4,572,192,429 49 | 428_466.mp4,540,132,299 50 | 438_434.mp4,542,148,316 51 | 439_441.mp4,462,127,279 52 | 441_439.mp4,305,120,272 53 | 463_464.mp4,315,112,269 54 | 471_455.mp4,540,148,333 55 | 484_415.mp4,319,124,283 56 | 516_555.mp4,354,134,311 57 | 521_517.mp4,304,109,261 58 | 526_436.mp4,838,230,489 59 | 528_510.mp4,377,121,291 60 | 532_544.mp4,391,130,307 61 | 566_617.mp4,723,128,284 62 | 569_921.mp4,335,108,256 63 | 584_823.mp4,475,182,419 64 | 604_703.mp4,825,148,335 65 | 611_760.mp4,645,136,289 66 | 623_630.mp4,352,135,311 67 | 632_548.mp4,427,177,390 68 | 640_638.mp4,306,118,271 69 | 649_816.mp4,930,181,386 70 | 653_601.mp4,731,149,331 71 | 715_721.mp4,856,259,549 72 | 717_684.mp4,374,157,344 73 | 731_741.mp4,778,130,315 74 | 744_674.mp4,294,127,274 75 | 748_355.mp4,318,111,270 76 | 752_751.mp4,1016,134,299 77 | 763_930.mp4,812,177,401 78 | 782_787.mp4,556,176,427 79 | 792_903.mp4,383,139,330 80 | 793_768.mp4,590,118,283 81 | 809_799.mp4,367,153,336 82 | 810_838.mp4,1052,189,417 83 | 814_871.mp4,550,136,310 84 | 829_808.mp4,801,270,588 85 | 831_508.mp4,327,120,283 86 | 870_001.mp4,604,177,407 87 | 872_873.mp4,474,201,438 88 | 877_886.mp4,570,202,464 89 | 878_866.mp4,736,197,451 90 | 894_848.mp4,328,135,299 91 | 899_914.mp4,560,122,275 92 | 907_795.mp4,525,198,460 93 | 915_895.mp4,352,126,273 94 | 916_783.mp4,484,173,374 95 | 918_934.mp4,472,145,333 96 | 927_912.mp4,697,262,585 97 | 952_882.mp4,700,148,314 98 | 957_959.mp4,418,165,374 99 | 969_897.mp4,356,133,299 100 | 996_056.mp4,312,113,269 101 | 999_960.mp4,335,142,309 102 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Undercover Deepfakes: Detecting Fake Segments in Videos 2 | 3 | Accepted at [DFAD Workshop](https://ailb-web.ing.unimore.it/dfad2023/) in ICCV 2023: [[arXiv](https://arxiv.org/abs/2305.06564) | [pdf](https://openaccess.thecvf.com/content/ICCV2023W/DFAD/papers/Saha_Undercover_Deepfakes_Detecting_Fake_Segments_in_Videos_ICCVW_2023_paper.pdf)] 4 | 5 | 6 | 7 | 8 | ## Evaluate on Temporal Deepfakes 9 | 1. Download the trained timeseries transformer model from [here](https://drive.google.com/drive/folders/1SNY-gIHY9QJigcYDR2115n7snv17W6nU?usp=sharing). 10 | 2. Also download the preprocessed data from [here](https://drive.google.com/drive/folders/1o1Z6l2Icrn2KV8SfieSHYBnSlnoewHpz?usp=sharing). Please note, this data corresponds to the _preprocessed_ **ViT-embeddings**, not raw images. 11 | 3. Run the script `evaluate.py` like below: 12 | ```shell 13 | python evaluate.py --model /model/temporal_dfd.h5 --data /embeddings/subtle/ --variation subtle 14 | ``` 15 | There are three types of embeddings: `subtle` for videos with carefully selected fake segments, `random` (TBA) for videos with randomly selected fake segments, and `video` (TBA) for videos that have same type of frames throughout i.e. they do not have a mix of `real` and `fake` frames. 16 | 17 | Use the argument `--data` and `--variation` accordingly i.e. if you change `--data` to the `random` directory, also change `--variation` to `random`. 18 | ## ViT Model Weights 19 | Fine-tuned ViT model weights can be found [here](https://mediaflux.researchsoftware.unimelb.edu.au:443/mflux/share.mfjp?_token=kMEoAeb6PUsHySXx7Ogw11282382393&browser=true&filename=checkpoint_best.pth.tar). 20 | ViT-embeddings for FF+ dataset can be downloaded [here](https://mediaflux.researchsoftware.unimelb.edu.au:443/mflux/share.mfjp?_token=TH4gLTKIH4bbwNECwkug11282382497&browser=true&filename=ff%2B_2_class_emb.zip). 21 | 22 | We also thank the authors of the [SSF](https://github.com/dongzelian/SSF) for providing their source code. 23 | 24 | 25 | ## Abstract 26 | The recent renaissance in generative models, driven primarily by the advent of diffusion models and iterative improvement in GAN methods, has enabled many creative applications. However, each advancement is also accompanied by a rise in the potential for misuse. In the arena of the deepfake generation, this is a key societal issue. In particular, the ability to modify segments of videos using such generative techniques creates a new paradigm of deepfakes which are mostly real videos altered slightly to distort the truth. This paradigm has been under-explored by the current deepfake detection methods in the academic literature. In this paper, we present a deepfake detection method that can address this issue by performing deepfake prediction at the frame and video levels. To facilitate testing our method, we prepared a new benchmark dataset where videos have both real and fake frame sequences with very subtle transitions. We provide a benchmark on the proposed dataset with our detection method which utilizes the Vision Transformer based on Scaling and Shifting to learn spatial features, and a Timeseries Transformer to learn temporal features of the videos to help facilitate the interpretation of possible deepfakes. Extensive experiments on a variety of deepfake generation methods show excellent results by the proposed method on temporal segmentation and classical video-level predictions as well. In particular, the paradigm we address will form a powerful tool for the moderation of deepfakes, where human oversight can be better targeted to the parts of videos suspected of being deepfakes. 27 | 28 | 29 | ## Temporal Dataset 30 | Temporal dataset is prepared based on the FaceForensics++ (FF++) dataset. We publish the start and end frame number of the fake segment(s) in the CSV files in [temporal_dataset](temporal_dataset/) folder. 31 | 32 | 33 | Manually selected fake segments where transition from real to fake frames and vice versa are very subtle. 34 | 35 | ## Model Architecture 36 | 37 | We leverage Parameter Efficient Fine-Tuning (PEFT) to build an efficient transformer-based architecture that achieves results comparable to and outperforming SOTA methods. 38 | 39 | 40 | 41 | 42 | ## Results 43 | ### Temporal Segmentation 44 | 45 | 46 | On the subset with subtle transitions between real and fake frames. 47 | 48 | 49 | 50 | 51 | On the subset with random (not subtle) transitions between real and fake frames. 52 | 53 | 54 | ### Video Level Classification 55 | 56 | 57 | 58 | 59 | ## Cite this paper 60 | @InProceedings{Saha_2023_ICCV, 61 | author = {Saha, Sanjay and Perera, Rashindrie and Seneviratne, Sachith and Malepathirana, Tamasha and Rasnayaka, Sanka and Geethika, Deshani and Sim, Terence and Halgamuge, Saman}, 62 | title = {Undercover Deepfakes: Detecting Fake Segments in Videos}, 63 | booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, 64 | month = {October}, 65 | year = {2023}, 66 | pages = {415-425} 67 | } 68 | -------------------------------------------------------------------------------- /evaluate.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from os.path import join 4 | from warnings import simplefilter 5 | 6 | import numpy as np 7 | from sklearn.metrics import roc_auc_score, accuracy_score 8 | from tensorflow.keras.models import load_model 9 | from tqdm import tqdm 10 | 11 | parser = argparse.ArgumentParser(description='FFpp Timeseries Training') 12 | parser.add_argument('--model', help='Path to model', required=True) 13 | parser.add_argument('--data', help='Path to data directory', required=True) 14 | parser.add_argument('--variation', help='Options: subtle, random, video', required=True) 15 | 16 | simplefilter(action='ignore', category=FutureWarning) 17 | simplefilter(action='ignore', category=UserWarning) 18 | 19 | 20 | def calculate_IOU(gt_labels, preds): 21 | intersection = sum(1 for gt, pred in zip(gt_labels, preds) if gt == pred) 22 | union = intersection + (len(gt_labels) - intersection) * 2 23 | return intersection / union 24 | 25 | 26 | def smooth_predictions(preds, offset=3): 27 | updated_preds = [] 28 | for i, pred in enumerate(preds): 29 | ll = max(0, i - offset) # left offset 30 | preds_l = updated_preds[ll:i] 31 | preds_r = preds[i + 1:i + offset + 1] 32 | 33 | majority_l = max(preds_l, key=preds_l.count) if len(preds_l) > 0 else -1 34 | majority_r = max(preds_r, key=preds_r.count) if len(preds_r) > 0 else -1 35 | 36 | if majority_l < 0: # left doesn't exist 37 | new_pred = majority_r if pred != majority_r else pred 38 | elif majority_r < 0: # right doesn't exist 39 | new_pred = majority_l if pred != majority_l else pred 40 | else: 41 | if majority_l == majority_r: 42 | new_pred = majority_l if pred != majority_l else pred 43 | else: 44 | new_pred = pred 45 | updated_preds.append(new_pred) 46 | return updated_preds 47 | 48 | 49 | def evaluate_video_level(in_dir, model_path, smooth_n_frames=25, dataset_level=True): 50 | """ 51 | real => 0 52 | fake => 1 (binary) 53 | """ 54 | model = load_model(model_path) 55 | 56 | predictions, ground_truths = [], [] 57 | wrong_predictions, results_log, load_error_list = [], [], [] 58 | 59 | datasets = ['DF', 'FSh', 'F2F', 'NT', 'FS', 'real'] 60 | predictions_per_dataset = {'DF': [], 'FSh': [], 'F2F': [], 'NT': [], 'FS': [], 'real': []} 61 | ground_truths_per_dataset = {'DF': [], 'FSh': [], 'F2F': [], 'NT': [], 'FS': [], 'real': []} 62 | 63 | for video_name_npy in tqdm(os.listdir(in_dir)): 64 | try: 65 | data = np.load(join(in_dir, video_name_npy)) 66 | except ValueError: 67 | load_error_list.append(video_name_npy) 68 | continue 69 | dataset = video_name_npy.split('_')[1] if video_name_npy.split('_')[0] == 'fake' else 'real' 70 | x_test, y_test_list = data[:, :, 1:], data[:, 0, 0] 71 | y_test_list = y_test_list.tolist() 72 | 73 | y_pred_raw = model.predict(x_test) 74 | y_pred_list = list(np.argmax(y_pred_raw, axis=1)) 75 | if smooth_n_frames > 0: 76 | y_pred_list = smooth_predictions(list(y_pred_list), smooth_n_frames) 77 | 78 | y_preds = max(y_pred_list, key=y_pred_list.count) 79 | y_test = max(y_test_list, key=y_test_list.count) 80 | 81 | if dataset_level: 82 | predictions_per_dataset[dataset].append(y_preds) 83 | ground_truths_per_dataset[dataset].append(y_test) 84 | 85 | predictions.append(y_preds) 86 | ground_truths.append(y_test) 87 | 88 | if y_preds != y_test: 89 | wrong_predictions.append(video_name_npy) 90 | 91 | if dataset_level: 92 | for d in datasets: 93 | if d == 'real': 94 | continue 95 | d_pred = predictions_per_dataset[d] + predictions_per_dataset['real'] 96 | d_gt = ground_truths_per_dataset[d] + ground_truths_per_dataset['real'] 97 | d_auc_macro = roc_auc_score(d_gt, d_pred, average="macro") 98 | d_acc = accuracy_score(d_gt, d_pred) 99 | print(f'{d}, {d_acc:0.3f}, {d_auc_macro:0.3f}') 100 | 101 | acc = accuracy_score(ground_truths, predictions) 102 | auc = roc_auc_score(ground_truths, predictions, average="macro") 103 | print(f'Avg, {acc:0.3f}, {auc:0.3f}') 104 | 105 | 106 | def evaluate_temporal(in_dir, model_path, smooth_n_frames=25, variation='subtle'): 107 | """ 108 | Evaluate Temporal deepfakes 109 | real => 0, fake => 1 (binary) 110 | """ 111 | 112 | if variation == 'subtle': 113 | datasets = ['F2F', 'NT'] 114 | predictions_per_dataset = {'F2F': [], 'NT': []} 115 | ground_truths_per_dataset = {'F2F': [], 'NT': []} 116 | elif variation == 'random': 117 | datasets = ['fake_DF', 'fake_FSh', 'fake_F2F', 'fake_NT', 'fake_FS'] 118 | predictions_per_dataset = {'fake_DF': [], 'fake_FSh': [], 'fake_F2F': [], 'fake_NT': [], 'fake_FS': []} 119 | ground_truths_per_dataset = {'fake_DF': [], 'fake_FSh': [], 'fake_F2F': [], 'fake_NT': [], 'fake_FS': []} 120 | else: 121 | print('Invalid argument `variation`.') 122 | exit(1) 123 | 124 | predictions, ground_truths = [], [] 125 | model = load_model(model_path) 126 | 127 | for video_name_npy in tqdm(os.listdir(in_dir)): 128 | if variation == 'subtle': 129 | dataset = video_name_npy.split('_')[0] 130 | else: 131 | dataset = video_name_npy.split('_')[0] + '_' + video_name_npy.split('_')[1] 132 | 133 | data = np.load(join(in_dir, video_name_npy), allow_pickle=True) 134 | 135 | if variation == 'subtle': 136 | x_test, y_test = data[:, :, 1:769], data[:, 0, 0] 137 | else: 138 | x_test, y_test = data[:, :, 2:], data[:, 0, 0] 139 | x_test = x_test.astype(float) 140 | 141 | y_preds_raw = model.predict(x_test) 142 | y_preds = np.argmax(y_preds_raw, axis=1) 143 | if smooth_n_frames > 0: 144 | y_preds = smooth_predictions(list(y_preds), smooth_n_frames) 145 | 146 | predictions.extend(list(y_preds)) 147 | ground_truths.extend(list(y_test)) 148 | 149 | predictions_per_dataset[dataset].extend(list(y_preds)) 150 | ground_truths_per_dataset[dataset].extend(list(y_test)) 151 | 152 | print('Dataset, Accuracy, IoU, AUC') 153 | for d in datasets: 154 | d_pred = predictions_per_dataset[d] 155 | d_gt = ground_truths_per_dataset[d] 156 | d_auc = roc_auc_score(d_gt, d_pred, average="macro") 157 | d_acc = accuracy_score(d_gt, d_pred) 158 | d_iou = calculate_IOU(d_gt, d_pred) 159 | print(f'{d}, {d_acc:0.3f}, {d_iou:0.3f}, {d_auc:0.3f}') 160 | 161 | auc_macro = roc_auc_score(ground_truths, predictions, average="macro") 162 | acc = accuracy_score(ground_truths, predictions) 163 | iou = calculate_IOU(ground_truths, predictions) 164 | print(f'Avg, {acc:0.3f}, {iou:0.3f}, {auc_macro:0.3f}') 165 | 166 | 167 | if __name__ == '__main__': 168 | args = vars(parser.parse_args()) 169 | 170 | model_path = str(args['model']) 171 | data_dir = str(args['data']) 172 | variation = str(args['variation']) # 'subtle' or 'random' or 'video' 173 | 174 | if not os.path.exists(model_path): 175 | print('Model does not exist.') 176 | exit(1) 177 | if not os.path.exists(data_dir): 178 | print('Data directory does not exist.') 179 | exit(1) 180 | if variation not in ['subtle', 'random', 'video']: 181 | print('Use \'subtle\' or \'random\' or \'video\' as variation.') 182 | exit(1) 183 | 184 | if variation == 'video': 185 | evaluate_video_level( 186 | in_dir=data_dir, 187 | model_path=model_path 188 | ) 189 | else: 190 | evaluate_temporal( 191 | in_dir=data_dir, 192 | model_path=model_path, 193 | variation=variation 194 | ) -------------------------------------------------------------------------------- /temporal_dataset/temporal_rand_one_segment_gt.csv: -------------------------------------------------------------------------------- 1 | dataset,video_name,total_frames,fake_start,fake_end 2 | Deepfakes,635_642,764,323,198 3 | Deepfakes,407_374,504,354,204 4 | Deepfakes,146_256,1003,339,214 5 | Deepfakes,533_450,558,342,217 6 | Deepfakes,535_587,667,459,284 7 | Deepfakes,250_461,564,321,171 8 | Deepfakes,058_039,669,421,246 9 | Deepfakes,512_495,750,364,214 10 | Deepfakes,540_536,957,471,296 11 | Deepfakes,101_096,1148,422,247 12 | Deepfakes,298_279,569,419,269 13 | Deepfakes,090_086,687,371,246 14 | Deepfakes,173_171,721,418,243 15 | Deepfakes,935_733,540,286,136 16 | Deepfakes,051_332,717,405,230 17 | Deepfakes,321_288,567,297,147 18 | Deepfakes,338_336,889,338,163 19 | Deepfakes,187_234,986,398,223 20 | Deepfakes,060_088,844,326,176 21 | Deepfakes,381_376,578,401,226 22 | Deepfakes,178_598,521,313,163 23 | Deepfakes,189_200,512,268,143 24 | Deepfakes,912_927,647,434,259 25 | Deepfakes,503_756,674,366,191 26 | Deepfakes,495_512,573,278,153 27 | Deepfakes,879_963,839,351,226 28 | Deepfakes,075_977,613,402,252 29 | Deepfakes,525_509,517,373,198 30 | Deepfakes,856_881,1103,391,216 31 | Deepfakes,853_863,616,342,192 32 | Deepfakes,742_775,623,401,276 33 | Deepfakes,616_614,663,285,135 34 | Deepfakes,478_506,749,466,316 35 | Deepfakes,724_725,600,371,196 36 | Deepfakes,729_727,636,393,218 37 | Deepfakes,206_221,714,340,190 38 | Deepfakes,497_403,621,440,290 39 | Deepfakes,639_841,662,358,183 40 | Deepfakes,526_436,838,348,173 41 | Deepfakes,721_715,581,446,271 42 | Deepfakes,233_995,675,435,260 43 | Deepfakes,050_059,530,375,200 44 | Deepfakes,304_300,610,380,205 45 | Deepfakes,605_591,664,336,161 46 | Deepfakes,280_249,606,332,157 47 | Deepfakes,356_324,558,347,197 48 | Deepfakes,257_420,763,519,344 49 | Deepfakes,506_478,668,390,265 50 | Deepfakes,642_635,573,281,131 51 | Deepfakes,782_787,556,304,154 52 | Deepfakes,234_187,528,401,226 53 | Deepfakes,123_119,514,389,239 54 | Deepfakes,734_699,1098,384,259 55 | Deepfakes,841_639,519,263,138 56 | Deepfakes,086_090,935,405,255 57 | Deepfakes,587_535,626,445,270 58 | Deepfakes,536_540,603,326,201 59 | Deepfakes,927_912,697,466,316 60 | 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dataset,video_name,total_frames,fake1_start,fake1_end,fake2_start,fake2_end 2 | Deepfakes,635_642,764,122,242,360,485 3 | Deepfakes,407_374,504,123,243,327,477 4 | Deepfakes,146_256,1003,73,198,345,470 5 | Deepfakes,533_450,558,84,209,317,442 6 | Deepfakes,535_587,667,112,237,366,491 7 | Deepfakes,250_461,564,83,208,304,454 8 | Deepfakes,058_039,669,88,208,317,467 9 | Deepfakes,512_495,750,74,194,354,504 10 | Deepfakes,540_536,957,83,208,324,474 11 | Deepfakes,101_096,1148,80,200,297,447 12 | Deepfakes,298_279,569,59,184,316,441 13 | Deepfakes,090_086,687,97,217,380,505 14 | Deepfakes,173_171,721,110,230,298,423 15 | Deepfakes,935_733,540,119,239,281,406 16 | Deepfakes,051_332,717,117,242,391,541 17 | Deepfakes,321_288,567,98,223,306,431 18 | Deepfakes,338_336,889,90,210,278,403 19 | Deepfakes,187_234,986,80,200,306,431 20 | Deepfakes,060_088,844,67,192,343,468 21 | Deepfakes,381_376,578,64,189,275,400 22 | Deepfakes,178_598,521,53,173,299,449 23 | Deepfakes,189_200,512,105,225,310,460 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Deepfakes,356_324,558,108,233,311,436 48 | Deepfakes,257_420,763,110,235,406,531 49 | Deepfakes,506_478,668,59,179,391,541 50 | Deepfakes,642_635,573,109,234,356,481 51 | Deepfakes,782_787,556,76,201,270,420 52 | Deepfakes,234_187,528,86,206,282,407 53 | Deepfakes,123_119,514,77,202,307,457 54 | Deepfakes,734_699,1098,87,207,271,421 55 | Deepfakes,841_639,519,92,212,286,411 56 | Deepfakes,086_090,935,71,191,395,545 57 | Deepfakes,587_535,626,86,206,386,511 58 | Deepfakes,536_540,603,73,193,339,489 59 | Deepfakes,927_912,697,77,202,377,527 60 | Deepfakes,715_721,856,59,179,298,423 61 | Deepfakes,468_470,597,93,213,328,453 62 | Deepfakes,519_515,751,62,187,439,564 63 | Deepfakes,398_457,582,92,212,282,432 64 | Deepfakes,282_238,594,120,245,307,457 65 | Deepfakes,391_406,547,55,180,331,456 66 | Deepfakes,450_533,774,56,176,290,440 67 | Deepfakes,405_393,531,124,244,285,410 68 | Deepfakes,332_051,695,89,209,374,524 69 | Deepfakes,645_688,554,58,183,343,468 70 | Deepfakes,494_445,802,118,243,437,587 71 | Deepfakes,857_909,640,53,178,389,514 72 | Deepfakes,217_117,702,114,239,280,430 73 | Deepfakes,749_659,693,65,190,392,542 74 | Deepfakes,716_712,628,106,226,278,428 75 | Deepfakes,128_896,735,98,218,311,461 76 | Deepfakes,119_123,502,115,235,271,421 77 | Deepfakes,436_526,518,95,215,312,437 78 | Deepfakes,819_786,927,96,216,314,464 79 | Deepfakes,725_724,699,73,193,321,471 80 | Deepfakes,775_742,559,105,225,328,478 81 | Deepfakes,672_720,541,103,228,308,433 82 | Deepfakes,923_023,602,75,200,323,473 83 | Deepfakes,712_716,550,89,214,277,402 84 | Deepfakes,171_173,541,67,192,338,488 85 | Deepfakes,289_228,597,63,188,286,411 86 | Deepfakes,237_236,757,59,184,387,537 87 | Deepfakes,346_351,615,62,187,356,481 88 | Deepfakes,221_206,718,56,181,357,482 89 | Deepfakes,727_729,554,106,231,325,475 90 | Deepfakes,591_605,541,119,244,302,452 91 | Deepfakes,367_371,649,89,209,338,488 92 | Deepfakes,376_381,515,73,198,302,452 93 | Deepfakes,866_878,509,73,193,278,428 94 | Deepfakes,699_734,524,120,245,313,463 95 | Deepfakes,238_282,622,96,221,346,471 96 | Deepfakes,368_378,761,64,189,392,542 97 | Deepfakes,515_519,766,90,215,379,529 98 | Deepfakes,808_829,637,91,216,333,458 99 | Deepfakes,877_886,570,102,227,307,432 100 | Deepfakes,720_672,564,72,192,316,466 101 | Deepfakes,420_257,1126,88,213,387,512 102 | FaceShifter,635_642,764,122,242,360,485 103 | FaceShifter,407_374,504,123,243,327,477 104 | FaceShifter,146_256,1003,73,198,345,470 105 | FaceShifter,533_450,558,84,209,317,442 106 | FaceShifter,535_587,667,112,237,366,491 107 | FaceShifter,250_461,564,83,208,304,454 108 | FaceShifter,058_039,669,88,208,317,467 109 | FaceShifter,512_495,750,74,194,354,504 110 | FaceShifter,540_536,957,83,208,324,474 111 | FaceShifter,101_096,1148,80,200,297,447 112 | FaceShifter,298_279,569,59,184,316,441 113 | FaceShifter,090_086,687,97,217,380,505 114 | FaceShifter,173_171,721,110,230,298,423 115 | FaceShifter,935_733,540,119,239,281,406 116 | FaceShifter,051_332,717,117,242,391,541 117 | FaceShifter,321_288,567,98,223,306,431 118 | FaceShifter,338_336,889,90,210,278,403 119 | FaceShifter,187_234,986,80,200,306,431 120 | FaceShifter,060_088,844,67,192,343,468 121 | FaceShifter,381_376,578,64,189,275,400 122 | FaceShifter,178_598,521,53,173,299,449 123 | FaceShifter,189_200,512,105,225,310,460 124 | FaceShifter,912_927,647,82,202,361,486 125 | FaceShifter,503_756,674,117,242,325,475 126 | FaceShifter,495_512,573,111,236,323,448 127 | FaceShifter,879_963,839,93,218,397,522 128 | FaceShifter,075_977,613,60,180,365,515 129 | FaceShifter,525_509,517,57,182,286,411 130 | FaceShifter,856_881,1103,52,172,372,497 131 | FaceShifter,853_863,616,112,237,318,468 132 | FaceShifter,742_775,623,72,197,305,430 133 | FaceShifter,616_614,663,95,220,301,451 134 | FaceShifter,478_506,749,122,247,389,539 135 | FaceShifter,724_725,600,70,190,333,483 136 | FaceShifter,729_727,636,55,175,282,407 137 | FaceShifter,206_221,714,86,211,403,528 138 | FaceShifter,497_403,621,70,190,375,500 139 | FaceShifter,639_841,662,60,180,272,422 140 | FaceShifter,526_436,838,50,175,304,429 141 | FaceShifter,721_715,581,87,207,317,467 142 | FaceShifter,233_995,675,55,175,315,440 143 | FaceShifter,050_059,530,80,205,325,450 144 | FaceShifter,304_300,610,62,182,353,478 145 | FaceShifter,605_591,664,85,205,287,412 146 | FaceShifter,280_249,606,106,231,359,509 147 | FaceShifter,356_324,558,108,233,311,436 148 | FaceShifter,257_420,763,110,235,406,531 149 | FaceShifter,506_478,668,59,179,391,541 150 | FaceShifter,642_635,573,109,234,356,481 151 | FaceShifter,782_787,556,76,201,270,420 152 | FaceShifter,234_187,528,86,206,282,407 153 | FaceShifter,123_119,514,77,202,307,457 154 | FaceShifter,734_699,1098,87,207,271,421 155 | FaceShifter,841_639,519,92,212,286,411 156 | FaceShifter,086_090,935,71,191,395,545 157 | FaceShifter,587_535,626,86,206,386,511 158 | FaceShifter,536_540,603,73,193,339,489 159 | FaceShifter,927_912,697,77,202,377,527 160 | FaceShifter,715_721,856,59,179,298,423 161 | FaceShifter,468_470,597,93,213,328,453 162 | FaceShifter,519_515,751,62,187,439,564 163 | FaceShifter,398_457,582,92,212,282,432 164 | FaceShifter,282_238,594,120,245,307,457 165 | FaceShifter,391_406,547,55,180,331,456 166 | FaceShifter,450_533,774,56,176,290,440 167 | FaceShifter,405_393,531,124,244,285,410 168 | FaceShifter,332_051,695,89,209,374,524 169 | FaceShifter,645_688,554,58,183,343,468 170 | FaceShifter,494_445,802,118,243,437,587 171 | FaceShifter,857_909,640,53,178,389,514 172 | FaceShifter,217_117,702,114,239,280,430 173 | FaceShifter,749_659,693,65,190,392,542 174 | FaceShifter,716_712,628,106,226,278,428 175 | FaceShifter,128_896,735,98,218,311,461 176 | FaceShifter,119_123,502,115,235,271,421 177 | FaceShifter,436_526,518,95,215,312,437 178 | FaceShifter,819_786,927,96,216,314,464 179 | FaceShifter,725_724,699,73,193,321,471 180 | FaceShifter,775_742,559,105,225,328,478 181 | FaceShifter,672_720,541,103,228,308,433 182 | FaceShifter,923_023,602,75,200,323,473 183 | FaceShifter,712_716,550,89,214,277,402 184 | FaceShifter,171_173,541,67,192,338,488 185 | FaceShifter,289_228,597,63,188,286,411 186 | FaceShifter,237_236,757,59,184,387,537 187 | FaceShifter,346_351,615,62,187,356,481 188 | FaceShifter,221_206,718,56,181,357,482 189 | FaceShifter,727_729,554,106,231,325,475 190 | FaceShifter,591_605,541,119,244,302,452 191 | FaceShifter,367_371,649,89,209,338,488 192 | FaceShifter,376_381,515,73,198,302,452 193 | FaceShifter,866_878,509,73,193,278,428 194 | FaceShifter,699_734,524,120,245,313,463 195 | FaceShifter,238_282,622,96,221,346,471 196 | FaceShifter,368_378,761,64,189,392,542 197 | FaceShifter,515_519,766,90,215,379,529 198 | FaceShifter,808_829,637,91,216,333,458 199 | FaceShifter,877_886,570,102,227,307,432 200 | FaceShifter,720_672,564,72,192,316,466 201 | FaceShifter,420_257,1126,88,213,387,512 202 | Face2Face,635_642,573,122,242,360,485 203 | Face2Face,407_374,586,123,243,327,477 204 | Face2Face,146_256,546,73,198,345,470 205 | Face2Face,533_450,774,84,209,317,442 206 | Face2Face,535_587,626,112,237,366,491 207 | Face2Face,250_461,612,83,208,304,454 208 | Face2Face,058_039,609,88,208,317,467 209 | Face2Face,512_495,573,74,194,354,504 210 | Face2Face,540_536,603,83,208,324,474 211 | Face2Face,101_096,504,80,200,297,447 212 | Face2Face,298_279,906,59,184,316,441 213 | Face2Face,090_086,935,97,217,380,505 214 | Face2Face,173_171,541,110,230,298,423 215 | Face2Face,935_733,518,119,239,281,406 216 | Face2Face,051_332,695,117,242,391,541 217 | Face2Face,321_288,543,98,223,306,431 218 | Face2Face,338_336,507,90,210,278,403 219 | Face2Face,187_234,528,80,200,306,431 220 | Face2Face,060_088,665,67,192,343,468 221 | Face2Face,381_376,515,64,189,275,400 222 | Face2Face,178_598,686,53,173,299,449 223 | Face2Face,189_200,719,105,225,310,460 224 | Face2Face,912_927,697,82,202,361,486 225 | Face2Face,503_756,570,117,242,325,475 226 | Face2Face,495_512,750,111,236,323,448 227 | Face2Face,879_963,692,93,218,397,522 228 | Face2Face,075_977,651,60,180,365,515 229 | Face2Face,525_509,563,57,182,286,411 230 | Face2Face,856_881,638,52,172,372,497 231 | Face2Face,853_863,513,112,237,318,468 232 | Face2Face,742_775,559,72,197,305,430 233 | Face2Face,616_614,507,95,220,301,451 234 | Face2Face,478_506,668,122,247,389,539 235 | Face2Face,724_725,699,70,190,333,483 236 | Face2Face,729_727,554,55,175,282,407 237 | Face2Face,206_221,718,86,211,403,528 238 | Face2Face,497_403,707,70,190,375,500 239 | Face2Face,639_841,519,60,180,272,422 240 | Face2Face,526_436,518,50,175,304,429 241 | Face2Face,721_715,856,87,207,317,467 242 | Face2Face,233_995,548,55,175,315,440 243 | Face2Face,050_059,721,80,205,325,450 244 | Face2Face,304_300,598,62,182,353,478 245 | Face2Face,605_591,541,85,205,287,412 246 | Face2Face,280_249,632,106,231,359,509 247 | Face2Face,356_324,1249,108,233,311,436 248 | Face2Face,257_420,1126,110,235,406,531 249 | Face2Face,506_478,749,59,179,391,541 250 | Face2Face,642_635,764,109,234,356,481 251 | Face2Face,782_787,502,76,201,270,420 252 | Face2Face,234_187,986,86,206,282,407 253 | Face2Face,123_119,502,77,202,307,457 254 | Face2Face,734_699,524,87,207,271,421 255 | Face2Face,841_639,662,92,212,286,411 256 | Face2Face,086_090,687,71,191,395,545 257 | Face2Face,587_535,667,86,206,386,511 258 | Face2Face,536_540,957,73,193,339,489 259 | Face2Face,927_912,647,77,202,377,527 260 | Face2Face,715_721,581,59,179,298,423 261 | Face2Face,468_470,905,93,213,328,453 262 | Face2Face,519_515,766,62,187,439,564 263 | Face2Face,398_457,528,92,212,282,432 264 | Face2Face,282_238,622,120,245,307,457 265 | Face2Face,391_406,512,55,180,331,456 266 | Face2Face,450_533,558,56,176,290,440 267 | Face2Face,405_393,579,124,244,285,410 268 | Face2Face,332_051,717,89,209,374,524 269 | Face2Face,645_688,571,58,183,343,468 270 | Face2Face,494_445,1053,118,243,437,587 271 | Face2Face,857_909,637,53,178,389,514 272 | Face2Face,217_117,548,114,239,280,430 273 | Face2Face,749_659,1001,65,190,392,542 274 | Face2Face,716_712,550,106,226,278,428 275 | Face2Face,128_896,531,98,218,311,461 276 | Face2Face,119_123,514,115,235,271,421 277 | Face2Face,436_526,838,95,215,312,437 278 | Face2Face,819_786,520,96,216,314,464 279 | Face2Face,725_724,600,73,193,321,471 280 | Face2Face,775_742,623,105,225,328,478 281 | Face2Face,672_720,564,103,228,308,433 282 | Face2Face,923_023,553,75,200,323,473 283 | Face2Face,712_716,628,89,214,277,402 284 | Face2Face,171_173,721,67,192,338,488 285 | Face2Face,289_228,556,63,188,286,411 286 | Face2Face,237_236,651,59,184,387,537 287 | Face2Face,346_351,684,62,187,356,481 288 | Face2Face,221_206,714,56,181,357,482 289 | Face2Face,727_729,636,106,231,325,475 290 | Face2Face,591_605,664,119,244,302,452 291 | Face2Face,367_371,718,89,209,338,488 292 | Face2Face,376_381,578,73,198,302,452 293 | Face2Face,866_878,736,73,193,278,428 294 | Face2Face,699_734,1098,120,245,313,463 295 | Face2Face,238_282,594,96,221,346,471 296 | Face2Face,368_378,761,64,189,392,542 297 | Face2Face,515_519,751,90,215,379,529 298 | Face2Face,808_829,801,91,216,333,458 299 | Face2Face,877_886,525,102,227,307,432 300 | Face2Face,720_672,541,72,192,316,466 301 | Face2Face,420_257,763,88,213,387,512 302 | NeuralTextures,635_642,573,122,242,360,485 303 | NeuralTextures,407_374,504,123,243,327,477 304 | NeuralTextures,146_256,546,73,198,345,470 305 | NeuralTextures,533_450,558,84,209,317,442 306 | NeuralTextures,535_587,626,112,237,366,491 307 | NeuralTextures,250_461,564,83,208,304,454 308 | NeuralTextures,058_039,609,88,208,317,467 309 | NeuralTextures,512_495,573,74,194,354,504 310 | NeuralTextures,540_536,603,83,208,324,474 311 | NeuralTextures,101_096,504,80,200,297,447 312 | NeuralTextures,298_279,569,59,184,316,441 313 | NeuralTextures,090_086,687,97,217,380,505 314 | NeuralTextures,173_171,541,110,230,298,423 315 | NeuralTextures,935_733,518,119,239,281,406 316 | NeuralTextures,051_332,695,117,242,391,541 317 | NeuralTextures,321_288,543,98,223,306,431 318 | NeuralTextures,338_336,507,90,210,278,403 319 | NeuralTextures,187_234,528,80,200,306,431 320 | NeuralTextures,060_088,665,67,192,343,468 321 | NeuralTextures,381_376,515,64,189,275,400 322 | NeuralTextures,178_598,521,53,173,299,449 323 | NeuralTextures,189_200,512,105,225,310,460 324 | NeuralTextures,912_927,647,82,202,361,486 325 | NeuralTextures,503_756,570,117,242,325,475 326 | NeuralTextures,495_512,573,111,236,323,448 327 | NeuralTextures,879_963,692,93,218,397,522 328 | NeuralTextures,075_977,613,60,180,365,515 329 | NeuralTextures,525_509,517,57,182,286,411 330 | NeuralTextures,856_881,638,52,172,372,497 331 | NeuralTextures,853_863,513,112,237,318,468 332 | NeuralTextures,742_775,559,72,197,305,430 333 | NeuralTextures,616_614,507,95,220,301,451 334 | NeuralTextures,478_506,668,122,247,389,539 335 | NeuralTextures,724_725,600,70,190,333,483 336 | NeuralTextures,729_727,554,55,175,282,407 337 | NeuralTextures,206_221,714,86,211,403,528 338 | NeuralTextures,497_403,621,70,190,375,500 339 | NeuralTextures,639_841,519,60,180,272,422 340 | NeuralTextures,526_436,518,50,175,304,429 341 | NeuralTextures,721_715,581,87,207,317,467 342 | NeuralTextures,233_995,548,55,175,315,440 343 | NeuralTextures,050_059,530,80,205,325,450 344 | NeuralTextures,304_300,598,62,182,353,478 345 | NeuralTextures,605_591,541,85,205,287,412 346 | NeuralTextures,280_249,606,106,231,359,509 347 | NeuralTextures,356_324,558,108,233,311,436 348 | NeuralTextures,257_420,763,110,235,406,531 349 | NeuralTextures,506_478,668,59,179,391,541 350 | NeuralTextures,642_635,573,109,234,356,481 351 | NeuralTextures,782_787,502,76,201,270,420 352 | NeuralTextures,234_187,528,86,206,282,407 353 | NeuralTextures,123_119,502,77,202,307,457 354 | NeuralTextures,734_699,524,87,207,271,421 355 | NeuralTextures,841_639,519,92,212,286,411 356 | NeuralTextures,086_090,687,71,191,395,545 357 | NeuralTextures,587_535,626,86,206,386,511 358 | NeuralTextures,536_540,603,73,193,339,489 359 | NeuralTextures,927_912,647,77,202,377,527 360 | NeuralTextures,715_721,581,59,179,298,423 361 | NeuralTextures,468_470,597,93,213,328,453 362 | NeuralTextures,519_515,751,62,187,439,564 363 | NeuralTextures,398_457,528,92,212,282,432 364 | NeuralTextures,282_238,594,120,245,307,457 365 | NeuralTextures,391_406,512,55,180,331,456 366 | NeuralTextures,450_533,558,56,176,290,440 367 | NeuralTextures,405_393,531,124,244,285,410 368 | NeuralTextures,332_051,695,89,209,374,524 369 | NeuralTextures,645_688,554,58,183,343,468 370 | NeuralTextures,494_445,802,118,243,437,587 371 | NeuralTextures,857_909,637,53,178,389,514 372 | NeuralTextures,217_117,548,114,239,280,430 373 | NeuralTextures,749_659,693,65,190,392,542 374 | NeuralTextures,716_712,550,106,226,278,428 375 | NeuralTextures,128_896,531,98,218,311,461 376 | 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