在潜空间中通过特征操纵生成语义对立示例
IEEE transactions on neural networks and learning systems
|August 10, 2023
概括
这项研究引入了一种新的对抗性攻击,通过操纵图像语义来针对深度神经网络 (DNN). 这种新方法创造了对黑子分类器有效的通用语义对抗示例.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习安全 机器学习安全
背景情况:
- 深度神经网络 (DNN) 容易受到对抗性攻击,其中微妙的干扰会误导分类器.
- 传统攻击使用非结构化的像素变化,经常改变图像外观不自然,缺乏可解释性.
研究的目的:
- 开发一种新的对抗性攻击策略,专注于语义,结构图像扰乱.
- 提高对DNN的对抗性示例的解释性和有效性.
主要方法:
- 提出了一种方法,通过解散的隐藏代码来操纵语义属性.
- 通过改变单个或组合的潜在代码来设计对抗性干扰.
- 引入了两个无监督的语义操纵策略:向量解和特征地图解的表示.
主要成果:
- 证明了攻击对现实世界图像数据的强大作用,特别是对黑子分类器.
- 经验评估证实了面向语义的结构性扰动的有效性.
- 确立了一个普遍的语义对立示例的存在,独立于特定的训练数据.
结论:
- 拟议的语义操纵攻击对DNN构成强大的威胁,特别是在黑子场景中.
- 语义对立的例子提供了一个更易于解释和潜在的更强大的攻击向量.
- 发现普遍的语义对立例子对人工智能安全研究有重大影响.
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