Black-box Universal Adversarial Perturbations for Image and Video Quality Assessment Methods

Most neural-network-based image- and video-quality-assessment metrics exhibit state-of-the-art performance; recent research, however, exposes their vulnerability to adversarial attacks. These attacks manipulate input data to inflate metric scores without enhancing visual quality. Although current research predominantly focuses on white-box adversarial attacks, black-box attacks remain underdeveloped despite their practical relevance, such as targeting metrics that are nondifferentiable or whose weights are unavailable. This paper adapts 10 black-box adversarial attacks, originally designed for image classifiers, to evaluate their effectiveness against image- and video-quality-assessment metrics. We tested them on 18 quality metrics and demonstrated their susceptibility to adversarial manipulations. Nonetheless, the practical utility of these attacks is limited by their computational complexity. To address this problem, we incorporated universal adversarial perturbations (UAPs) into five black-box attacks. Our proposed UAP attacks, despite providing less attack strength than the originals, still effectively increase the metric scores and are viable for real-time applications. Our code is available at https://github.com/georgebychkov/black-box-iqa.