Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics
<|reference_start|>Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics: In the field of Image Quality Assessment (IQA), the adversarial robustness of the metrics poses a critical concern. This paper presents a comprehensive benchmarking study of various defense mechanisms in response to the rise in adversarial attacks on IQA. We systematically evaluate 25 defense strategies, including adversarial purification, adversarial training, and certified robustness methods. We applied 14 adversarial attack algorithms of various types in both non-adaptive and adaptive settings and tested these defenses against them. We analyze the differences between defenses and their applicability to IQA tasks, considering that they should preserve IQA scores and image quality. The proposed benchmark aims to guide future developments and accepts submissions of new methods, with the latest results available online: https://videoprocessing.ai/benchmarks/iqa-defenses.html.<|reference_end|>
@article{gushchin2024guardians,
    title={Guardians of Image Quality: Benchmarking Defenses Against Adversarial
  Attacks on Image Quality Metrics},
    author={Alexander Gushchin, Khaled Abud, Georgii Bychkov, Ekaterina
  Shumitskaya, Anna Chistyakova, Sergey Lavrushkin, Bader Rasheed, Kirill
  Malyshev, Dmitriy Vatolin, Anastasia Antsiferova},
    journal={arXiv preprint arXiv:2408.01541},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01541},
    primaryClass={cs.CV eess.IV}
}