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 metrics like
Spearman's Rank-Order Correlation Coefficient (SROCC) and prediction
error-related metrics like Mean Squared Error (MSE). As an instantiation, we
propose a practical two-stage SROCC-MSE-Attack (SMA) that initially optimizes
target attack scores for the entire image set and then generates adversarial
examples guided by these scores. Experimental results demonstrate that our SMA
method not only significantly disrupts the SROCC to negative values but also
maintains a considerable change in the scores of individual images. Meanwhile,
it exhibits state-of-the-art performance across metrics with different
categories. Our method provides a new perspective on the robustness of NR-IQA
models.

深度神经网络在无参考图像质量评估中取得了令人印象深刻的成功，但最近的研究强调了 NR-IQA 模型对微小对抗扰动的脆弱性，导致模型预测与主观评分之间的不一致性。当前的对抗攻击忽视了整个图像集内得分之间的关联关系，本研究提出了一种基于关联误差的攻击框架，同时破坏图像集内的关联性和个体图像的得分变化。该研究主要关注 Spearman 等排名相关的关联度和 Mean Squared Error 等预测误差度量。通过实验证明，该方法显著破坏了 Spearman 相关系数并对个体图像的得分产生了明显的变化，同时在不同类别的指标上表现出了最先进的性能，为 NR-IQA 模型的鲁棒性提供了新的视角。