Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Yi Yu, Wenhan Yang, Yap-Peng Tan, Alex C. Kot

TL;DR
This paper conducts a comprehensive analysis of the robustness of deep learning-based rain removal methods against adversarial attacks, revealing vulnerabilities and proposing a more robust approach.
Contribution
It provides the first extensive empirical evaluation of rain removal methods under adversarial attacks and introduces a more robust deraining technique based on these insights.
Findings
Rain removal methods are more vulnerable to attacks on highly degraded images.
Certain modules in existing methods can be improved for robustness.
Proposed method enhances resistance to adversarial perturbations.
Abstract
Rain removal aims to remove rain streaks from images/videos and reduce the disruptive effects caused by rain. It not only enhances image/video visibility but also allows many computer vision algorithms to function properly. This paper makes the first attempt to conduct a comprehensive study on the robustness of deep learning-based rain removal methods against adversarial attacks. Our study shows that, when the image/video is highly degraded, rain removal methods are more vulnerable to the adversarial attacks as small distortions/perturbations become less noticeable or detectable. In this paper, we first present a comprehensive empirical evaluation of various methods at different levels of attacks and with various losses/targets to generate the perturbations from the perspective of human perception and machine analysis tasks. A systematic evaluation of key modules in existing methods is…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Image Enhancement Techniques · Fire Detection and Safety Systems
