3D-Aware Adversarial Makeup Generation for Facial Privacy Protection（面向面部隐私保护的3D感知对抗化妆生成）
3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
AI总结：
本文提出3DAM-GAN框架，通过基于UV的生成器和集成训练策略生成高质量、高迁移性的对抗化妆，有效保护面部隐私免受各类人脸识别模型和商业API的攻击。
摘要：
社交媒体上面部数据的隐私与安全正面临前所未有的挑战，因为其容易遭受未经授权的访问和识别。解决此问题的常见做法是修改原始数据，使其免受恶意人脸识别（FR）系统的识别。然而，现有方法获得的此类“对抗样本”通常存在迁移性低和图像质量差的问题，严重限制了这些方法在真实场景中的应用。在本文中，我们提出了一种3D-Aware Adversarial Makeup Generation GAN（3D感知对抗化妆生成GAN，3DAM-GAN），旨在提高用于身份信息隐藏的合成化妆的质量和迁移性。具体而言，设计了一个基于UV的生成器，由新颖的Makeup Adjustment Module（化妆调整模块，MAM）和Makeup Transfer Module（化妆迁移模块，MTM）组成，借助人脸的对称特征渲染逼真且鲁棒的化妆。此外，提出了一种带有集成训练策略的化妆攻击机制，以提升黑盒模型的迁移性。在几个基准数据集上的大量实验结果表明，3DAM-GAN能有效保护面部免受各种FR模型的攻击，包括公开可用的最先进模型和商业人脸验证API，如Face++、Baidu和Aliyun。
英文摘要：
The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification. A common practice for solving this problem is to modify the original data so that it could be protected from being recognized by malicious face recognition (FR) systems. However, such ``adversarial examples'' obtained by existing methods usually suffer from low transferability and poor image quality, which severely limits the application of these methods in real-world scenarios. In this paper, we propose a 3D-Aware Adversarial Makeup Generation GAN (3DAM-GAN). which aims to improve the quality and transferability of synthetic makeup for identity information concealing. Specifically, a UV-based generator consisting of a novel Makeup Adjustment Module (MAM) and Makeup Transfer Module (MTM) is designed to render realistic and robust makeup with the aid of symmetric characteristics of human faces. Moreover, a makeup attack mechanism with an ensemble training strategy is proposed to boost the transferability of black-box models. Extensive experiment results on several benchmark datasets demonstrate that 3DAM-GAN could effectively protect faces against various FR models, including both publicly available state-of-the-art models and commercial face verification APIs, such as Face++, Baidu and Aliyun.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
