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基于自然封闭的后门攻击:一种新的方法来破坏行人探测器
Qiong Li1,2, Yalun Wu1,2, Qihuan Li1,2
1School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种新的后门攻击,用于行人检测系统,使用真实世界的遮,如背包作为触发器. 该方法成功绕过了物理测试中的检测,突出了自动驾驶安全方面的漏洞.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能安全 的人工智能安全
- 机器学习 稳固性 机器学习 稳固性
背景情况:
- 深度神经网络 (DNN) 对于自动驾驶等安全关键应用中的行人检测至关重要.
- 对DNN的现有后门攻击通常使用非自然的数字触发器,限制了它们在现实世界的隐形性和适用性.
- 对于行人检测系统对物理可实现的后门攻击的脆弱性尚未得到充分了解.
研究的目的:
- 为行人检测系统提出和评估一种新的后门攻击方法,使用自然,现实世界的遮作为触发器.
- 在各种场景中展示物理部署后门攻击的可行性和有效性.
- 提供关于提高行人检测系统对此类威胁的物理强度的见解.
主要方法:
- 开发了一个后门攻击,利用常见的现实世界遮蔽 (例如,背包) 作为自然触发器.
- 设计了一个动态启发式策略,用于适应性触发器放置和缩放,以适应各种阻塞场景.
- 实施了三种无模型触发器嵌入技术,并使用KITTI和CityPersons数据集对两种行人检测模型进行了实验.
主要成果:
- 在保持基线性能的同时,实现了高的攻击成功率 (KITTI上为75.1%,CityPersons上为97.1%).
- 物理测试证实,在不同的摄像头距离下,使用背包触发器成功逃避检测.
- 分析了关于触发模式,中毒率和防御抵抗力的攻击有效性.
结论:
- 在行人检测中,常见的物理阻塞可以有效地被用作后门攻击的自然触发器.
- 提出的方法证明了实际可行性,并对自主系统的安全性构成重大威胁.
- 研究结果强调,需要开发强大的防御机制,以抵御计算机视觉系统中物理可实现的后门攻击.
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