University of Oulu

Chen, B., Liu, X., Xia, Z., & Zhao, G. (2023). Privacy-preserving DeepFake face image detection. Digital Signal Processing, 143, 104233.

Privacy-preserving DeepFake face image detection

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Author: Chen, Beijing1,2; Liu, Xin1; Xia, Zhihua3;
Organizations: 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
Format: article
Version: accepted version
Access: embargoed
Persistent link:
Language: English
Published: Elsevier, 2023
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

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Series: Digital signal processing
ISSN: 1051-2004
ISSN-E: 1095-4333
ISSN-L: 1051-2004
Volume: 143
Article number: 104233
DOI: 10.1016/j.dsp.2023.104233
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 62072251), and the PAPD fund.
Copyright information: © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http:/