通过使用基于潜伏的干扰来调查步态识别模型的漏洞.
Zeeshan Ali1, Maryam Bukhari2, Mubashir Javaid3
1Department of Software Development and Automation, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
Scientific reports
|November 10, 2025
概括
这项研究引入了一种新的黑子攻击步态识别系统,增强安全监视. BLG攻击的成功率达到了94.33%,提供了一种现实的方法来测试模型漏洞.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 安全系统安全系统
背景情况:
- 视频监控对于安全至关重要,步态识别提供了独特的识别功能.
- 用于步态识别的深度学习模型容易受到对抗性攻击,这构成了重大安全挑战.
- 现有的攻击通常需要广泛的模型访问或缺乏现实世界的适用性.
研究的目的:
- 提出一种新的,实用的,可转移的黑子攻击步态识别系统.
- 在有限访问场景中开发有效和感知上现实的攻击方法.
- 评估步态识别模型对复杂的对抗性攻击的脆弱性.
主要方法:
- 引入了黑盒子潜伏GEI (BLG) 攻击,这是一个新的黑盒子对抗技术.
- 开发了AdvHelper,一个替代模型来模拟目标步态识别系统.
- 使用编码器-解码器框架实现了PerturbGen,用于实现现实的干扰的重建和感知损失.
主要成果:
- 在CASIA-gait数据集上,BLG攻击的成功率高达94.33%.
- 生成的对抗性样本既有效又在感知上现实.
- 在现实的监控环境中证明了黑子攻击的可行性.
结论:
- 拟议的BLG攻击在理解步态识别中的对抗性漏洞方面取得了重大进展.
- 该方法为评估模型稳定性提供了一种实用和可转移的方法.
- 强调需要开发更具弹性的步态识别系统来应对复杂的对抗性威胁.
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