Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop

• URL: http://arxiv.org/abs/2210.00933v1
• Date: Mon, 3 Oct 2022 13:47:16 GMT
• Title: Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
• Authors: Weixia Zhang and Dingquan Li and Xiongkuo Min and Guangtao Zhai and Guodong Guo and Xiaokang Yang and Kede Ma
• Abstract summary: We make one of the first attempts to examine the perceptual robustness of NR-IQA models. We test one knowledge-driven and three data-driven NR-IQA methods under four full-reference IQA models. We find that all four NR-IQA models are vulnerable to the proposed perceptual attack.
• Score: 113.75573175709573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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