FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. Selfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised
82 PAPERS • 1 BENCHMARK
FaceForensics++ is a forensics dataset consisting of 1000 original video sequences that have been manipulated with four automated face manipulation methods: Deepfakes, Face2Face, FaceSwap and NeuralTextures The data has been sourced from 977 youtube videos and all videos contain a trackable mostly frontal face without occlusions which enables automated tampering methods to generate realistic forgeries.
315 PAPERS • 2 BENCHMARKS
…The fake videos are created using 6 different methods: FaceSwap, DeepFaceLab, FSGAN, FOMM, ATFHP and Wav2Lip.
17 PAPERS • NO BENCHMARKS YET
Human face Deepfake dataset sampled from large datasets High Quality Dataset Diverse Dataset Challenging Dataset Large Dataset Text prompts
1 PAPER • NO BENCHMARKS YET
DeeperForensics-1.0 represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind The source videos are collected on 100 paid and consented actors from 26 countries, and the manipulated videos are generated by a newly proposed many-to-many end-to-end face swapping method, DF-VAE. 7
21 PAPERS • NO BENCHMARKS YET
We construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 videos), manipulations (7 image-level approaches, 8 video-level approaches We perform extensive benchmarking and studies of existing face forensics methods and obtain several valuable observations.
3 PAPERS • 2 BENCHMARKS
WildDeepfake is a dataset for real-world deepfakes detection which consists of 7,314 face sequences extracted from 707 deepfake videos that are collected completely from the internet.
34 PAPERS • NO BENCHMARKS YET
…The images inside each zip file are face-only. We provide three convenient sizes: 224 x 224, 512 x 512, and 1024 x 1024 pixels. The participant covers their eyes with a hand, followed by covering the left half, the right half, and finally, the lower half of the face. The participant moves a green cloth in front of their face The participant puts on a face mask and counts from 1 to 10 out loud. Then, they remove the facemask. FSGAN (Face Swapping Generative Adversarial Network): This corresponds to the second version of FSGAN. Access its release at https://github.com/wyhsirius/LIA As a rule of thumb, An imposter outer face and target is the inner face, in case of faceswaps.