  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.
