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face-forgery-detection

This is the author's personal implementaion of the core two-stream model from Generalizing Face Forgery Detection with High-frequency Features (CVPR 2021).

For more details, please refer to the original [paper].

Overview

In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection.

We carefully devise three functional modules to take full advantage of the high-frequency features.

  • The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality.
  • The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective.
  • The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other.

The two-stream model is shown as follows.

image-20210428105010020

Dependency

The model is implemented with PyTorch.

Pretrained Xception weights are downloaded from this link.

Contact

Please contact - LUO Yuchen - [email protected]

Citation

@InProceedings{Luo_2021_CVPR,
    author    = {Luo, Yuchen and Zhang, Yong and Yan, Junchi and Liu, Wei},
    title     = {Generalizing Face Forgery Detection With High-Frequency Features},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {16317-16326}
}

Notice

Thank you all for using this repo! I've received several emails regarding to the implementation and reproducing issues. Here I list some tips that might be useful :).

  • Training and testing are conducted following the specifications in FaceForensics++ paper.
  • The training datasets can be downloaded and created following the official instructions of FaceForensics++.
  • The cross-dataset performance is largely influenced by the training data scale and training time. I found that the GPU version, the training batchsize, and the distributing setting also have impacts on the performance. Please refer to the detailed specifications in the paper.