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潜在的自动驾驶威胁:普遍的雨水清除攻击

Jincheng Hu1, Jihao Li1, Zhuoran Hou1

  • 1Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire LE11 3TU UK.

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概括

一个新的普遍雨去除攻击 (URA) 利用自动驾驶系统的神经网络漏洞. 这种攻击显著降低了图像质量和场景恢复,在恶劣天气中对车辆的感知构成威胁.

关键词:
人工智能的人工智能是人工智能.人工智能应用的人工智能应用.计算机科学 计算机科学

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 自动驾驶自动驾驶的自动驾驶

背景情况:

  • 恶劣的天气挑战了自动驾驶的感知.
  • 图像雨去除算法使用神经网络来恢复清晰的图像.
  • 现有的算法缺乏对抗攻击的稳定性.

研究的目的:

  • 提出一项针对图像雨除算法的普遍雨除攻击 (URA).
  • 调查自动驾驶感知系统中神经网络的脆弱性.
  • 评估URA在降低图像质量和场景恢复方面的有效性.

主要方法:

  • 开发了一种使用非添加空间扰动的普遍雨水清除攻击 (URA).
  • 将URA应用于最先进的图像去雨算法.
  • 量化了URA对场景修复相似性和图像质量的影响.

主要成果:

  • URA显著降低了场景修复的相似性和图像质量.
  • 攻击使现场修复能力降低了39.5%.
  • 图像生成质量降低了26.4%.

结论:

  • URA是一种有效的方法来检测图像雨移除算法的漏洞.
  • 由于URA的不可察觉性和通用性,这表明可能会发生真实世界的人工智能攻击.
  • 在恶劣天气条件下,增强强性对AI感知系统至关重要.