/CLRNet

One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework

Primary LanguageJupyter NotebookMIT LicenseMIT

Overview

Title: One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework [Accepted at WWW '21]

CLRNet-pipeline

Citation

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}
}

Additional Results

Updated in-domain attack results including DFDC dataset

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

Table3

Updated out-of-domain attack results (before using our defense strategy)

  • 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.

Supplementary-DFDC-OOD

Dataset used for Evaluation

Models used for Evaluation