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

翻译：现代基于神经网络的无参考图像与视频质量指标表现出与全参考指标相当的性能。这些指标被广泛用于改进计算机视觉方法中的视觉质量以及比较视频处理方法。然而，这些指标对传统对抗攻击不够稳定，可能产生错误结果。我们的目标是探究无参考质量指标的适用边界，本文提出一种针对无参考质量指标的快速对抗扰动攻击方法。所提出的攻击方法（FACPA）可作为实时视频处理与压缩算法的预处理步骤。本研究可为设计稳定的基于神经网络的无参考质量指标提供有益启示。