Adversarial purification for no-reference image-quality metrics: applicability study and new methods
<|reference_start|>Adversarial purification for no-reference image-quality metrics: applicability study and new methods: Recently, the area of adversarial attacks on image quality metrics has begun to be explored, whereas the area of defences remains under-researched. In this study, we aim to cover that case and check the transferability of adversarial purification defences from image classifiers to IQA methods. In this paper, we apply several widespread attacks on IQA models and examine the success of the defences against them. The purification methodologies covered different preprocessing techniques, including geometrical transformations, compression, denoising, and modern neural network-based methods. Also, we address the challenge of assessing the efficacy of a defensive methodology by proposing ways to estimate output visual quality and the success of neutralizing attacks. Defences were tested against attack on three IQA metrics -- Linearity, MetaIQA and SPAQ. The code for attacks and defences is available at: (link is hidden for a blind review).<|reference_end|>
@article{gushchin2024adversarial,
    title={Adversarial purification for no-reference image-quality metrics:
  applicability study and new methods},
    author={Aleksandr Gushchin, Anna Chistyakova, Vladislav Minashkin, Anastasia
  Antsiferova and Dmitriy Vatolin},
    journal={arXiv preprint arXiv:2404.06957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06957},
    primaryClass={cs.CV cs.AI}
}