Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
Chenxi Yang, Yujia Liu, Dingquan Li, Yan Zhong, Tingting Jiang

TL;DR
This paper introduces a novel adversarial attack framework targeting no-reference image quality assessment models, focusing on disrupting both individual image scores and inter-score correlations, revealing their vulnerability.
Contribution
It proposes a correlation-error-based attack framework and a two-stage SROCC-MSE-Attack method that effectively compromises NR-IQA models' robustness.
Findings
Significantly reduces SROCC to negative values
Maintains substantial changes in individual image scores
Achieves state-of-the-art attack performance across metrics
Abstract
Deep neural networks have demonstrated impressive success in No-Reference Image Quality Assessment (NR-IQA). However, recent researches highlight the vulnerability of NR-IQA models to subtle adversarial perturbations, leading to inconsistencies between model predictions and subjective ratings. Current adversarial attacks, however, focus on perturbing predicted scores of individual images, neglecting the crucial aspect of inter-score correlation relationships within an entire image set. Meanwhile, it is important to note that the correlation, like ranking correlation, plays a significant role in NR-IQA tasks. To comprehensively explore the robustness of NR-IQA models, we introduce a new framework of correlation-error-based attacks that perturb both the correlation within an image set and score changes on individual images. Our research primarily focuses on ranking-related correlation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced X-ray and CT Imaging · Image and Video Quality Assessment
MethodsSparse Evolutionary Training · Focus · Slime Mould Algorithm
