  The problem of robustness in adverse weather conditions is considered a
significant challenge for computer vision algorithms in the applicants of
autonomous driving. Image rain removal algorithms are a general solution to
this problem. They find a deep connection between raindrops/rain-streaks and
images by mining the hidden features and restoring information about the
rain-free environment based on the powerful representation capabilities of
neural networks. However, previous research has focused on architecture
innovations and has yet to consider the vulnerability issues that already exist
in neural networks. This research gap hints at a potential security threat
geared toward the intelligent perception of autonomous driving in the rain. In
this paper, we propose a universal rain-removal attack (URA) on the
vulnerability of image rain-removal algorithms by generating a non-additive
spatial perturbation that significantly reduces the similarity and image
quality of scene restoration. Notably, this perturbation is difficult to
recognise by humans and is also the same for different target images. Thus, URA
could be considered a critical tool for the vulnerability detection of image
rain-removal algorithms. It also could be developed as a real-world artificial
intelligence attack method. Experimental results show that URA can reduce the
scene repair capability by 39.5% and the image generation quality by 26.4%,
targeting the state-of-the-art (SOTA) single-image rain-removal algorithms
currently available.
