与局部内核的卷积中的转换对称性导致对高频对抗示例的隐性偏见
Josue O Caro1, Yilong Ju1,2, Ryan Pyle1,2
1Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States.
Frontiers in computational neuroscience
|July 5, 2024
概括
卷积神经网络 (CNN) 经常学习高频特征,导致对抗性攻击的脆弱性. 这项研究表明,CNN中的局部内核是这些高频对抗示例的关键原因.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 敌对攻击对神经网络安全构成重大威胁.
- 敌对性扰动经常表现出高频特征,但根本原因尚未完全理解.
研究的目的:
- 调查转换对称性和卷积运算中的局部内核有助于高频对抗示例的假设.
- 分析不同神经网络架构对隐性偏差和对抗性干扰的影响.
主要方法:
- 在空间和频率领域分析线性和非线性架构.
- 核子大小和模型深度对对抗性扰动频率的影响的评估.
- 卷积运算与视觉变压器和MLP式网络等替代架构的比较.
主要成果:
- 与其他架构相比,卷积操作本质上产生更高频率的对抗性攻击,无论培训数据集如何.
- 增加的内核位置 (较小的内核大小) 和模型深度加剧了高频偏差.
- 避免卷积或使用更大的内核可以显著降低高频偏差,符合富里埃不确定性原理.
结论:
- 神经网络架构的隐性偏差,特别是卷积中的本地化内核,是高频对抗示例的主要驱动因素.
- 了解和减轻这种架构偏见对于开发强大的对抗防御机制至关重要.
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