  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.
