A Local Perturbation Generation Method for GAN-Generated Face Anti-Forensics

Abstract

Although the current generative adversarial networks (GAN)-generated face forensic detectors based on deep neural networks (DNNs) have achieved considerable performance, they are vulnerable to adversarial attacks. In this paper, an effective local perturbation generation method is proposed to expose the vulnerability of state-of-the-art forensic detectors. The main idea is to mine the fake faces' areas of common concern in multiple-detectors' decision-making, then generate local anti-forensic perturbations by GANs in these areas to enhance the visual quality and transferability of anti-forensic faces. Meanwhile, in order to improve the anti-forensic effect, a doublemask (soft mask and hard mask) strategy and a three-part loss (the GAN training loss, the adversarial loss consisting of ensemble classification loss and ensemble feature loss, and the regularization loss) are designed for the training of the generator. Experiments conducted on fake faces generated by StyleGAN demonstrate the proposed method's advantage over the state-of-the-art methods in terms of anti-forensic success rate, imperceptibility, and transferability. The source code is available at https://github.com/imagecbj/A-Local-Perturbation-Generation-Method-for-GAN-generated-Face-Anti-forensics.

Meta Generative Attack on Person Reidentification

“…The tremendous performance of deep learning models has led to rampant application of these systems in practice. However, these models can be manipulated by introducing minor perturbations [1]- [5]. This process is called adversarial attacks.…”

Systematic Review: Anti-Forensic Computer Techniques

“…Ding et al [47][48][49][50][51]53] propose anti-forensic tools and methods by which to bypass "DeepFake" detection on videos. On the other hand, Zhao, X et al [52,54] apply it to "DeepFake" detection in GAN-generated images.…”

“…Studies [47][48][49][50][51][52][53][54] propose different strategies and models for generating DeepFakes that can evade forensic detection. Paper [47] propose GAN models with additional features and loss functions designed to improve visual quality and model efficiency.…”

“…Paper [47] propose GAN models with additional features and loss functions designed to improve visual quality and model efficiency. Article [48] employs local perturbations to expose the vulnerability of forensic detectors, while article [49] uses a bidirectional conversion between computer-generated and natural face images. Articles [50,51] focus on adversarial attacks for DeepFake detectors, reducing detection accuracy and highlighting the need to consider visual perception in the generation of these attacks.…”