敌对攻击如何破坏看似稳定的准确分类器
Oliver J Sutton1, Qinghua Zhou1, Ivan Y Tyukin1
1Department of Mathematics, King's College London, London, UK.
概括
敌对攻击利用输入数据的修改来欺骗精确的机器学习系统. 对随机噪声的稳定性并不能阻止这些攻击,这是高维数据分类器的一个关键特征.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 敌对攻击对机器学习系统构成重大威胁.
- 对于随机扰动有力的系统往往仍然容易受到对抗的例子的影响.
- 这一漏洞对于在高维数据上运行的分类器尤其令人担忧.
研究的目的:
- 调查分类器同时易受对抗性攻击和对随机扰动的稳定性背后的根本原因.
- 引入一种通用框架,解释这些观察到的行为在实际系统中.
- 为了在用于图像分类的现实世界神经网络中证实这些现象.
主要方法:
- 开发一个简单的,可通用的理论框架.
- 使用在标准图像分类任务中训练有素的神经网络进行实证验证.
- 分析随机扰动与对抗性扰动对分类器输出的影响.
主要成果:
- 该框架表明,对抗性易感性和随机稳定性是高维数据分类器固有的特征.
- 实际的神经网络表现出相同的行为,在大量随机噪音下保持稳定,但易受对手攻击的影响.
- 随机扰动测试时,小的决策边际可以掩盖对抗性易感性.
结论:
- 敌对脆弱性是高维空间中的分类器的一个基本特征.
- 随机噪声对于检测或减轻对立例子是无效的.
- 有效的防御需要更强大的对手训练方法.
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