Abstract
The rapid growth of social media has led to the widespread sharing of individual portrait images, which pose serious privacy risks due to the capabilities of automatic face recognition (AFR) systems for mass surveillance. Hence, protecting facial privacy against unauthorized AFR systems is essential. Inspired by the generation capability of the emerging diffusion models, recent methods employ diffusion models to generate adversarial face images for privacy protection. However, they suffer from the diffusion purification effect, leading to a low protection success rate (PSR). In this paper, we first propose learning unconditional embeddings to increase the learning capacity for adversarial modifications and then use them to guide the modification of the adversarial latent code to weaken the diffusion purification effect. Moreover, we integrate an identity-preserving structure to maintain structural consistency between the original and generated images, allowing human observers to recognize the generated image as having the same identity as the original. Extensive experiments conducted on two public datasets, i.e., CelebA-HQ and LADN, demonstrate the superiority of our approach. The protected faces generated by our method outperform those produced by existing facial privacy protection approaches in terms of transferability and natural appearance.
Abstract (translated)
社交媒体的快速增长导致个人肖像图片广泛分享，这给自动面部识别（AFR）系统的大规模监控带来了严重的隐私风险。因此，保护面部隐私以防止未经授权的AFR系统至关重要。受到新兴扩散模型生成能力的启发，最近的方法使用扩散模型来生成对抗性人脸图像以实现隐私保护。然而，这些方法受到了扩散净化效应的影响，导致了较低的成功保护率（PSR）。在这篇论文中，我们首先提出了学习无条件嵌入以增强针对对抗性修改的学习容量，并利用它们引导对抗性潜在代码的修改，从而削弱扩散净化效应。此外，我们整合了一个保持身份特征的结构，在原始图像和生成图像之间维持结构性的一致性，使人类观察者能够识别出生成的图片与原图具有相同的个体身份。在两个公共数据集CelebA-HQ和LADN上进行的广泛实验表明了我们的方法的优越性。我们方法生成的受保护面孔在可迁移性和自然外观方面超过了现有的面部隐私保护方法。
URL
https://arxiv.org/abs/2503.10350
