关键词: 对抗攻击, 可逆神经网络, GAN生成人脸, 反取证, 黑盒

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.

Key words: adversarial attack, invertible neural network, GANgenerated faces, antiforensics, blackbox