敌对的红外曲线:对物理世界的红外行人探测器的攻击
Chengyin Hu1, Weiwen Shi2, Wen Yao3
1University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Gaoxin District, Chengdu, 611731, Sichuan, China; Chinese Academy of Military Science, No. 53 East Street, Fengtai District, Beijing, 100071, China.
概括
研究人员开发了Adversarial Infrared Curves (AdvIC),以提高对红外攻击的深度神经网络安全性. 这种新的方法提高了攻击的现实性和稳定性,解决了当前可见光和红外线安全研究的局限性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 的安全性至关重要,但红外物理攻击仍未得到充分探索.
- 现有的红外攻击方法缺乏现实性,隐形性和强度.
研究的目的:
- 为红外领域的DNN提出一种新的物理攻击方法.
- 为了提高红外敌对攻击的隐形性和稳定性.
主要方法:
- 敌对红外曲线 (AdvIC) 使用粒子集群优化来优化贝齐尔曲线.
- 使用冷补丁引入物理干扰,以产生红外线曲线图案.
- 该方法的重点是为攻击创建现实的物理样本.
主要成果:
- AdvIC实现了很高的攻击成功率:94.8% (数字) 和67.2% (物理).
- 与基线方法相比,证明了更高的隐形性和稳定性.
- 对各种先进的探测器实现了平均76.2%的成功率.
结论:
- 在针对DNN的红外对抗性攻击方面,AdvIC取得了重大进展.
- 该方法的有效性和稳定性对现实世界视觉系统具有重大安全影响.
- 需要紧急关注和缓解策略来解决这些安全漏洞.
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