3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
Yueming Lyu Yue Jiang Ziwen He Bo Peng Yunfan Liu Jing Dong
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.