信息安全研究2025，Vol.11Issue(5)：394-401,8.DOI:10.12379/j.issn.2096-1057.2025.05.01
基于可逆神经网络的黑盒GAN生成人脸反取证方法
A Black-box Anti-forensics Method of GAN-generated Faces Based on Invertible Neural Network
摘要
Abstract
Generative adversarial network GAN-generated faces forensics models are used to distinguish real faces and GAN-generated faces.But due to the fact that forensics models are susceptible to adversarial attacks,the anti-forensics techniques for GAN-generated faces have emerged.However,existing anti-forensic methods rely on white-box surrogate models,which have limited transferability.Therefore,a black-box method based on invertible neural network(INN)is proposed for GAN-generated faces anti-forensics in this paper.This method embeds the features of real faces into GAN-generated faces through the INN,which enables the generated anti-forensics faces to disturb forensics models.Meanwhile,the proposed method introduces a feature loss during training to maximize the cosine similarity between the features of the anti-forensics faces and the real faces,further improving the attack performance of anti-forensics faces.Experimental results demonstrate that,under the scenarios where no white-box models are involved,the proposed method has good attack performance against eight GAN-generated faces forensics models with better performance than seven comparative methods,and can generate high-quality anti-forensics faces.关键词
对抗攻击/可逆神经网络/GAN生成人脸/反取证/黑盒Key words
adversarial attack/invertible neural network/GAN-generated faces/anti-forensics/black-box分类
计算机与自动化引用本文复制引用
陈北京,冯逸凡,李玉茹..基于可逆神经网络的黑盒GAN生成人脸反取证方法[J].信息安全研究,2025,11(5):394-401,8.基金项目
国家自然科学基金项目(62072251) （62072251）
