3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
- URL: http://arxiv.org/abs/2306.14640v1
- Date: Mon, 26 Jun 2023 12:27:59 GMT
- Title: 3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
- Authors: Yueming Lyu and Yue Jiang and Ziwen He and Bo Peng and Yunfan Liu and
Jing Dong
- Abstract summary: 3D-Aware Adversarial Makeup Generation GAN (3DAM-GAN)
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.
Experiment results on several benchmark datasets demonstrate that 3DAM-GAN could effectively protect faces against various FR models.
- Score: 23.915259014651337
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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