通过选择性地保存扰乱的低频组件来优化通信信号的对立例子
Yi Zhang1,2, Lulu Wang1, Xiaolei Wang1
1Intelligent Game and Decision Lab, Beijing 100071, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究引入了一种新方法,通过专注于低频扰动元件 (LFCP) 来创建更好的通信信号对抗示例. 这种方法尽量减少扭曲,同时保持高攻击成功率 (ASR).
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 机器学习中的对抗性示例旨在实现高攻击成功率 (ASR) 与最小的扭曲.
- 优化对通信信号的对抗示例是一个重大挑战.
研究的目的:
- 提出一种用于优化通信信号对抗的新方法.
- 专注于低频扰动元件 (LFCP) 进行改进优化.
主要方法:
- 分析模型对DCT系数的注意力,以了解LFCP的作用.
- 开发一个基于维护LFCP的优化策略.
- 采用二进制搜索算法与模型预测不一致性作为限制来识别LFCP.
主要成果:
- 拟议的方法显著降低了FGSM,BIM,PGD和MI-FGSM攻击的扰乱扭曲.
- 通过拟议的方法观察到ASR的轻微改善.
- 这种方法即使在使用小扰动的DeepFool和BS-FGM等攻击中也显示出可行的性能.
结论:
- 维护LFCP是优化通信信号中的对抗性示例的一个基本概念.
- 基于二进制搜索的方法有效地平衡了最小化扭曲和保持ASR.
- 该方法为生成强大的对抗性示例提供了一个实际的解决方案.
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