区域导向攻击对分段的任何东西模型
Xiaoliang Liu1, Furao Shen2, Jian Zhao3
1School of Information Engineering, Wenzhou Business College, China.
概括
分段任何模型 (SAM) 容易受到对抗性攻击. 一个新的区域引导攻击 (RGA) 通过针对区域有效地操纵图像细分,导致SAM输出中的错误.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 细分任何模型 (SAM) 是一个领先的图像细分工具,对自动驾驶和医学成像至关重要.
- SAM容易受到对抗性攻击,在这种情况下,小的输入变化会导致显著的性能下降.
- 现有的对抗性攻击方法往往不适合细分任务,无法利用空间细微差别或内部结构依赖.
研究的目的:
- 开发一种专门为分段任何模型 (SAM) 量身定制的新型对抗性攻击战略.
- 通过利用模型固有的结构特征来解决细分中的当前对抗技术的局限性.
主要方法:
- 引入区域导向攻击 (RGA),这是针对SAM设计的一种新方法.
- 利用区域导向地图 (RGM) 来指导细分区域内的目标扰动.
- 实施RGA以分割大型细分并扩展较小的细分,诱导错误的细分输出.
主要成果:
- 在白盒和黑盒对抗性攻击场景中,RGA表现出高的成功率.
- 该攻击有效地通过利用特定区域的漏洞来操纵SAM的细分.
- 实验验证证证实了RGA在损害SAM性能方面的有效性.
结论:
- 拟议的区域引导攻击 (RGA) 对分段任何模型 (SAM) 构成重大威胁.
- 发展强大的防御机制,以应对像RGA这样复杂的对抗性攻击,是非常必要的.
- 这些发现强调了理解和减轻高级图像细分模型中的漏洞的重要性.
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