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Nowadays, neural-network-based image- and video-quality metrics performbetter than traditional methods. However, they also became more vulnerable toadversarial attacks that increase metrics' scores without improving visualquality. The existing benchmarks of quality metrics compare their performancein terms of correlation with subjective quality and calculation time.Nonetheless, the adversarial robustness of image-quality metrics is also anarea worth researching. This paper analyses modern metrics' robustness todifferent adversarial attacks. We adapted adversarial attacks from computervision tasks and compared attacks' efficiency against 15 no-reference image-and video-quality metrics. Some metrics showed high resistance to adversarialattacks, which makes their usage in benchmarks safer than vulnerable metrics.The benchmark accepts submissions of new metrics for researchers who want tomake their metrics more robust to attacks or to find such metrics for theirneeds. The latest results can be found online:https://videoprocessing.ai/benchmarks/metrics-robustness.html.

Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks