Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
This work addresses the reliability of NR-IQA models used in vision systems, highlighting vulnerabilities that could impact their practical applications, though it is incremental as it builds on existing attack frameworks.
The authors investigated the perceptual robustness of no-reference image quality assessment (NR-IQA) models by developing a perceptual attack method, finding that all four tested models were vulnerable to it, with generated counterexamples being non-transferable and revealing distinct design flaws.
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA models are extensively studied in computational vision, and are widely used for performance evaluation and perceptual optimization of man-made vision systems. Here we make one of the first attempts to examine the perceptual robustness of NR-IQA models. Under a Lagrangian formulation, we identify insightful connections of the proposed perceptual attack to previous beautiful ideas in computer vision and machine learning. We test one knowledge-driven and three data-driven NR-IQA methods under four full-reference IQA models (as approximations to human perception of just-noticeable differences). Through carefully designed psychophysical experiments, we find that all four NR-IQA models are vulnerable to the proposed perceptual attack. More interestingly, we observe that the generated counterexamples are not transferable, manifesting themselves as distinct design flows of respective NR-IQA methods.