微妙的对抗性图像操纵影响人类和机器的感知
Vijay Veerabadran1,2, Josh Goldman1, Shreya Shankar1,3
1Google, Mountain View, CA, USA.
Nature communications
|August 15, 2023
概括
人工神经网络 (ANN) 是脆弱的,容易被对抗性干扰所欺骗. 人类的感知也对这些图像操纵很敏感,这表明了共同的潜在机制.
科学领域:
- 计算机视觉 计算机视觉
- 认知科学 认知科学
- 神经科学是一个神经科学.
背景情况:
- 人工神经网络 (ANN),受到大脑的启发,表现出与人类感知不同的脆弱性.
- ANN容易受到对抗性干扰,导致图像的错误分类.
- 相比之下,人类的感知往往会将这种扰动视为文物.
研究的目的:
- 调查人类对对抗性扰动的敏感性是否与ANN相似.
- 探索驱动人类和ANN对对抗性示例的反应的潜在机制.
主要方法:
- 呈现对抗性扰动,已知可以愚弄ANN,给人类观察者.
- 分析人类选择行为,以应对扰乱的图像.
- 在各种干扰中比较人类对ANN易受性的敏感性.
主要成果:
- 欺骗ANN的敌对干扰也影响了人类的选择.
- 人类对对抗性干扰的敏感性通过行为测量来证明.
- 这种效应似乎是由高阶图像统计驱动的,而不是ANN架构.
结论:
- 人类的感知与ANN分享对对抗性干扰的敏感性.
- 自然图像的高阶统计是人类和ANN感知中的关键因素.
- 这表明了生物和人工视觉处理的共同原则.
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