细分和恢复:保护对象探测器免受敌对补丁攻击
Haotian Gu1, Hamidreza Jafarnejadsani1
1Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA.
Journal of imaging
|September 26, 2025
概括
本研究介绍了细分和恢复 (SAR),这是对对象检测模型的对抗性补丁攻击的新型防御. 在动态环境中,SAR可以有效地检测和删除恶意补丁,而无需重新训练模型,从而提高关键应用程序的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 对象检测模型对于自动驾驶和安全等应用至关重要.
- 敌对补丁攻击对这些模型的可靠性构成重大威胁,特别是在动态环境中.
- 目前的防御系统在补丁尺寸,位置和方向的变化中扎.
研究的目的:
- 为对象检测模型开发针对对抗性补丁攻击的补丁不可知防御方案.
- 在动态和复杂的环境中解决现有防御的局限性.
- 在安全关键的应用中提高对象检测系统的稳定性.
主要方法:
- 引入了Segment and Recover (SAR),这是一个基于图像预处理的防御方案.
- 集成了一个无补丁检测前端与一个破碎的像素恢复后端.
- 开发了SAR来处理大面具覆盖物体隐藏攻击,并打破补丁规模,形状和位置的限制.
主要成果:
- SAR准确地定位对抗补丁并恢复受影响的像素.
- 评估表明SAR与各种预训练的物体探测器兼容.
- 与各种补丁类型相比,在最先进的方法上表现出显著的弹性改进.
结论:
- 在对象检测中,SAR提供了一种强大而可适应的防御,以抵御对手补丁攻击.
- 基于补丁的不可知性和基于预处理的方法消除了需要额外的模型培训的需求.
- 在现实世界,动态场景中,SAR提高了对象检测系统的可靠性和安全性.
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