AdvFaceGAN:一种基于生成对抗网络的双面身份假冒攻击方法
Hong Huang1, Yang Yang1, Yunfei Wang1
1School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin, Sichuan, China.
PeerJ. Computer science
|June 26, 2025
概括
AdvFaceGAN生成双重身份的对抗性面孔,以绕过面部识别安全性. 这种方法提高了现实场景的隐形性和攻击效率,提高了面部识别技术的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 目前的面部识别系统存在安全漏洞.
- 现有的数字和物理攻击方法在现实世界中存在局限性.
- 数字攻击缺乏实体实施,而实体攻击缺乏隐形.
研究的目的:
- 揭示商业面部识别系统中的安全漏洞.
- 促进面部识别技术安全方面的进步.
- 提出一种新的双重身份冒充攻击方法.
主要方法:
- AdvFaceGAN是一种基于生成对抗网络 (GAN) 的方法,可以生成双身份对抗面孔.
- 引入结构相似性损失以提高视觉质量和隐形性.
- 采用一组多样化的面部识别模型,并结合源身份丢失以提高攻击效率.
主要成果:
- AdvFaceGAN生成具有出色隐形性的敌对面孔,通过人类审查.
- 实现了优越的传统和双重身份假冒攻击成功率.
- 与现有方法相比,在黑子场景中表现出更好的隐形性和可转移性.
结论:
- AdvFaceGAN有效地解决了现有的假冒攻击方法的局限性.
- 拟议的方法通过突出漏洞来提高面部识别安全性.
- 对抗面部生成的进步可以提高面部识别系统的稳定性.
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