Privacy-preserving DeepFake face image detection
|Author:||Chen, Beijing1,2; Liu, Xin1; Xia, Zhihua3;|
1Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
3College of Cyber Security, Jinan University, Guangzhou, 510632, China
4Center for Machine Vision and Signal Analysis, University of Oulu, Oulu FI-90014, Finland
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231025141349
|Publish Date:|| 2025-10-02
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.
Digital signal processing
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was supported by the National Natural Science Foundation of China (Grant No. 62072251), and the PAPD fund.
© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http:/creativecommons.org/licenses/by-nc-nd/4.0/