Abstract

All the existing models for DeepFake detection focus on plaintext faces. However, outsourced computing is usually considered in practical applications for DeepFake detection and the input data may contain private and sensitive information. Thus, a privacy-preserving model named Secure DeepFake Detection Network (SecDFDNet) is proposed for the first time in this paper. The SecDFDNet uses the additive secret sharing method for secure DeepFake face detection. Specifically, firstly, some multi-party secure interaction protocols are designed for non-linear activation functions, i.e., SecReLU for ReLU function, SecSigm for sigmoid function, SecSpatial for spatial attention, and SecChannel for channel attention. Their security is proved in theory. Our protocols have low communication and space complexity. Then, the SecDFDNet model is proposed by using the designed secure protocols and trained plaintext DeepFake detection network (DFDNet). The experimental results show that the proposed SecDFDNet can detect DeepFake faces without revealing anything of private input, achieve the same accuracies as the plaintext DFDNet and outperform some existing models. The source code is available at https://github.com/imagecbj/Privacy-Preserving-DeepFake-Face-Image-Detection.