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Title: One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework (WWW '21) (arXiv)

If you find our work useful for your research, please consider citing the following papers :)

@inproceedings{tariq2021web,
  title={One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework},
  author={Tariq, Shahroz and Lee, Sangyup and Woo, Simon S},
  booktitle={Proceedings of The Web Conference 2021},
  year={2021},
  url = {https://doi.org/10.1145/3442381.3449809},
  doi = {10.1145/3442381.3449809}
}

The following link contains the weights for the models (CLRNet [CLR], ShallowNetV3 [SNV3], MesoInception4 [M14], and Xception [XCE]) used in our experiments

https://drive.google.com/drive/folders/1CE-HzZh76ejAsrIFSlbaEGmQHyzoj9EQ?usp=sharing

• Note that CLRNet performs the best for DFDC dataset among all the test baselines.

• Note that results from Table 5 demonstrates that models trained on DFDC, which is a quite generic and diverse dataset, can still fail to detect out-of-domain attack (see Table 5).
• See Table 6 in our paper, for defense performance against out-of-domain attack.

• Facial Reenactment
  • Face2Face (F2F) [Dataset] [Paper]
  • Neural Texture (NT) [Dataset] [Paper]
• Identity Swap
  • DeepFake (DF) [Dataset] [GitHub]
  • FaceSwap (FS) [Dataset] [GitHub]
  • DeepFake Detection (DFD) [Dataset] [Blog]
  • DeepFake Detection Challenge (DFDC) [Dataset] [Paper] [Kaggle Challenge] [Blog]
• Unknown
  • DeepFake in the Wild (DFW) [Sample Links] [Source1] [Source2] [Source3]

• Single Frame-based
  • ShallowNet [Paper] [GitHub]
  • Xception [Paper] [GitHub]
  • MesoNet [Paper] [GitHub]
• Multiple Frame-based
  • CNN+LSTM [Paper]
  • DBiRNN [Paper]
  • CLRNet (Ours)