隐形CMOS摄像头眼,用于对深度神经网络进行对抗性攻击
Zvi Stein1, Adir Hazan1, Adrian Stern1
1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
研究人员开发了一种新的看不见的光学攻击,通过使CMOS摄像头眼,欺骗深度神经网络 (DNN). 这种物理对抗性攻击利用相机快门机制来破坏图像而无需人类检测.
科学领域:
- 计算机视觉 计算机视觉
- 网络安全 网络安全
- 光学是什么?光学是什么?光学是什么?
背景情况:
- 深度神经网络 (DNN) 在性能方面表现出色,但容易受到对抗性攻击.
- 现有的物理对抗性攻击往往是人类可视检测的.
- 物理系统中的漏洞构成重大网络安全风险.
研究的目的:
- 引入一种新的,看不见的基于光学的物理对抗性攻击,针对CMOS摄像头.
- 分析必要的条件,使攻击对人眼不可察觉,但对DNNs有效.
- 调查光源参数与攻击效率之间的关系.
主要方法:
- 为光学攻击设计特定的光脉冲序列.
- 利用相机的快门机制在空间上转换图像中的光脉冲.
- 分析可见性和图像破坏的光学条件.
- 在不同的光源工作周期下对DNN的攻击成功率的评估.
主要成果:
- 在CMOS摄像头上展示了一个看不见的基于光学的物理对抗性攻击.
- 确定了最佳的光学条件,使攻击无法被人类发现.
- 展示了使用拟议的方法欺骗DNN的能力.
- 量化了基于光源工作周期的攻击成功和隐藏之间的权衡.
结论:
- 拟议的隐形光学攻击是欺骗物理世界DNN的一种可行的方法.
- 控制光源工作周期对于平衡攻击有效性和隐形性至关重要.
- 这项研究突出了基于摄像头的AI系统的新物理漏洞.
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