A Deepfake Face Image Detection Model Supporting Privacy Protection

Existing researches on deepfake face detection are all performed under plaintext conditions, while face images are with significant privacy. Therefore, a deepfake detection model with privacy-preserving properties is proposed based on the additive secret sharing framework. Firstly, four secure communication protocols are constructed on the basis of the existing fundamental computing protocols. Secondly, a non-colluding dual server is used to construct a plaintext-like environment. With the support of the constructed secure communication protocols, the pre-trained ResNet50 models in dual servers compute interactively and cooperatively. Finally, the results of the dual servers are merged to achieve secure deepfake detection without exposing the input. The security and correctness of the proposed protocols are proved by theoretical analysis. Experiments on the public datasets FaceForensics++, Celeb-DF and DFDC further prove that the proposed security detection model can achieve the same accuracy as its corresponding plaintext ResNet50 model under the premise of supporting privacy protection. Furthermore, the proposed privacy preservation model is also applicable to other plaintext state-of-the-art deepfake detection models, such as Xception and EfficientNet-B0.