一个基于光学遥感图像预测的界限框适应变形的对抗性示例攻击方法
Leyu Dai1,2,3, Jindong Wang1,2,3, Bo Yang1,2,3
1State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China.
PeerJ. Computer science
|June 10, 2024
概括
研究人员开发了一种自适应变形方法 (ADM) 来攻击光学遥感中的YOLO物体探测器. 这种新的方法改善了对实时检测系统的对抗性稳定性评估.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 遥感 遥感 遥感 遥感
背景情况:
- 现有的对抗性攻击对实时光学遥感物体探测器 (YOLO系列) 无效.
- 由于不合适的对抗性扰动机制,当前的方法与光学遥感图像作斗争.
- 提高单阶段探测器的对抗性稳定性仍然是一个挑战.
研究的目的:
- 提出一种新的自适应变形方法 (ADM),用于欺骗YOLO物体探测器.
- 在光学遥感中加强对抗YOLOv4和YOLOv5的攻击.
- 为评估对抗性弹性提供一个更有效的方案.
主要方法:
- 引入了自适应变形方法 (ADM) 来产生对抗性扰动.
- 开发了自适应变形方法 代快速渐变信号方法 (ADM-I-FGSM) 和自适应变形机制 预测渐变下降 (ADM-PGD).
- 利用预测框的长宽比来确定扰动的变形趋势.
主要成果:
- 与最先进的方法相比,拟议的ADM实现了更高的对抗性成功率.
- 基于ADM的攻击有效地欺骗YOLOv4和YOLOv5探测器.
- 生成的对抗性干扰显示出一个优异的对抗性效应.
结论:
- 适应变形方法为光学遥感中YOLO探测器的对抗性攻击提供了更有效的方法.
- 这种攻击方案为评估这些模型的对抗性弹性提供了有价值的工具.
- 这些发现强调了对抗复杂的敌对攻击需要强大的防御机制.
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