  No-reference image- and video-quality metrics are widely used in video
processing benchmarks. The robustness of learning-based metrics under video
attacks has not been widely studied. In addition to having success, attacks
that can be employed in video processing benchmarks must be fast and
imperceptible. This paper introduces an Invisible One-Iteration (IOI)
adversarial attack on no reference image and video quality metrics. We compared
our method alongside eight prior approaches using image and video datasets via
objective and subjective tests. Our method exhibited superior visual quality
across various attacked metric architectures while maintaining comparable
attack success and speed. We made the code available on GitHub:
https://github.com/katiashh/ioi-attack.
