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Potential auto-driving threat: Universal rain-removal attack.

Jincheng Hu1, Jihao Li1, Zhuoran Hou1

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

Iscience
|August 28, 2023
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Summary
This summary is machine-generated.

A new universal rain-removal attack (URA) exploits neural network vulnerabilities in autonomous driving systems. This attack significantly degrades image quality and scene restoration, posing a threat to vehicle perception in severe weather.

Keywords:
Artificial intelligenceArtificial intelligence applicationsComputer science

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Driving

Background:

  • Severe weather challenges autonomous driving perception.
  • Image rain-removal algorithms use neural networks to restore clear images.
  • Existing algorithms lack robustness against adversarial attacks.

Purpose of the Study:

  • To propose a universal rain-removal attack (URA) targeting image rain-removal algorithms.
  • To investigate the vulnerability of neural networks in autonomous driving perception systems.
  • To evaluate the effectiveness of URA in degrading image quality and scene restoration.

Main Methods:

  • Developed a universal rain-removal attack (URA) using non-additive spatial perturbation.
  • Applied URA to state-of-the-art image rain-removal algorithms.
  • Quantified the impact of URA on scene restoration similarity and image quality.

Main Results:

  • URA significantly diminishes scene restoration similarity and image quality.
  • The attack reduced scene repair capability by 39.5%.
  • Image generation quality was reduced by 26.4%.

Conclusions:

  • URA is an effective method for detecting vulnerabilities in image rain-removal algorithms.
  • The imperceptible and generic nature of URA suggests potential for real-world AI attacks.
  • Enhanced robustness is crucial for AI perception systems in adverse weather conditions.