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Modern neural-network-based no-reference image- and video-quality metricsexhibit performance as high as full-reference metrics. These metrics are widelyused to improve visual quality in computer vision methods and compare videoprocessing methods. However, these metrics are not stable to traditionaladversarial attacks, which can cause incorrect results. Our goal is toinvestigate the boundaries of no-reference metrics applicability, and in thispaper, we propose a fast adversarial perturbation attack on no-referencequality metrics. The proposed attack (FACPA) can be exploited as apreprocessing step in real-time video processing and compression algorithms.This research can yield insights to further aid in designing of stableneural-network-based no-reference quality metrics.

Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metrics