愚蠢的例子:神经网络的另一个有趣的属性
Ming Zhang1, Yongkang Chen1, Cheng Qian1
1National Key Laboratory of Science and Technology on Information System Security, Beijing 100101, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
神经网络可以被人类无法识别的"愚蠢的例子"所欺骗. 这些使用代方法生成的示例具有高度可转移性,并对人工智能系统构成安全风险.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 神经网络容易受到对抗性示例的影响,这些是模型错误分类的输入,但人类可以识别.
- 这一漏洞给神经网络的广泛应用带来了重大安全挑战.
研究的目的:
- 为了调查神经网络中的新漏洞:存在"愚蠢的例子",模型可以识别,但人类不能.
- 开发一种方法来生成这些愚蠢的例子,并评估它们的特性和可转移性.
主要方法:
- 一种代生成方法,灵感来自对抗性示例构建,被开发用于创建愚蠢的示例.
- 在白盒和黑盒场景中进行了实验,以评估生成成功率和跨模型的可转移性.
- 在真实世界的系统上进行测试,例如谷歌云视觉API.
主要成果:
- 在白盒设置中,欺骗示例的成功率接近100%.
- 这些愚蠢的例子在黑子场景中显示出强大的可转移性到不同的模型.
- 谷歌云视觉API也认识到这些愚蠢的例子,表明现实世界的适用性.
结论:
- 神经网络表现出以前未被识别的认知缺陷,相信不存在的对象.
- 很容易生成和可转移的愚蠢的例子突出显示潜在的安全威胁.
- 未来的神经网络应用程序必须解决这个漏洞,以确保强大而安全的AI系统.
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