基于高层特征差异的强大的对抗性示例检测算法
Hua Mu1, Chenggang Li2,3, Anjie Peng4
1College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究引入了一种使用高级特征差异 (HFD) 的新对抗性示例检测算法. 该方法增强了对各种攻击和预处理的稳定性,提高了深度学习安全性.
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:
- 对抗式示例 (AE) 对深度学习模型构成重大威胁.
- 现有的检测算法的准确性经常受到攻击特征和图像预处理的影响.
- 预处理对检测稳定性的影响仍未得到充分探索.
研究的目的:
- 提出一种新的对抗性示例检测算法,对攻击和预处理都可靠.
- 提高深度学习系统对复杂的对抗威胁的可靠性.
- 为了解决图像预处理对检测性能所忽视的影响.
主要方法:
- 开发了一种基于高级特征差异 (HFD) 的新型检测算法.
- 使用编码器从测试和对应训练图像中提取高级特征.
- 如果特征相似性很低,将图像归类为对抗性,利用语义冲突.
主要成果:
- 基于HFD的方法在FS,DF和MD检测算法上显示出显著的改进.
- 实现了与ESRM方法相匹配的检测准确性.
- 展现出对预处理操作的卓越稳定性,如降低样本和常见的腐败.
结论:
- 拟议的基于HFD的算法在各种攻击中提供了高检测准确度,同时保持了对预处理的弹性.
- 该方法适用于各种目标模型,为对抗性示例检测提供了有价值的新视角.
- 这项研究强调了在设计强大的防御机制时考虑预处理的重要性.
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