No-Reference Image Quality Assessment (NR-IQA) aims to predict image quality scores consistent with human perception without relying on pristine reference images, serving as a crucial component in various visual tasks. Ensuring the robustness of NR-IQA methods is vital for reliable comparisons of different image processing techniques and consistent user experiences in recommendations. The attack methods for NR-IQA provide a powerful instrument to test the robustness of NR-IQA. However, current attack methods of NR-IQA heavily rely on the gradient of the NR-IQA model, leading to limitations when the gradient information is unavailable. In this paper, we present a pioneering query-based black box attack against NR-IQA methods. We propose the concept of score boundary and leverage an adaptive iterative approach with multiple score boundaries. Meanwhile, the initial attack directions are also designed to leverage the characteristics of the Human Visual System (HVS). Experiments show our method outperforms all compared state-of-the-art attack methods and is far ahead of previous black-box methods. The effective NR-IQA model DBCNN suffers a Spearman's rank-order correlation coefficient (SROCC) decline of 0.6381 attacked by our method, revealing the vulnerability of NR-IQA models to black-box attacks. The proposed attack method also provides a potent tool for further exploration into NR-IQA robustness.

翻译：无参考图像质量评估（NR-IQA）旨在无需原始参考图像即可预测与人类感知一致的图像质量分数，是各种视觉任务中的关键组成部分。确保NR-IQA方法的鲁棒性对于不同图像处理技术的可靠比较以及推荐系统中一致的用户体验至关重要。针对NR-IQA的攻击方法为测试其鲁棒性提供了有力工具。然而，当前NR-IQA的攻击方法严重依赖模型的梯度信息，导致在梯度信息不可用时存在局限性。本文首次提出了一种基于查询的黑盒攻击方法，针对NR-IQA方法展开探索。我们引入了分数边界的概念，并利用自适应迭代方法结合多个分数边界。同时，初始攻击方向也设计为利用人类视觉系统（HVS）的特性。实验结果表明，我们的方法优于所有对比的最先进攻击方法，并大幅领先于先前的黑盒方法。受我们方法攻击后，有效的NR-IQA模型DBCNN的斯皮尔曼秩相关系数（SROCC）下降了0.6381，揭示了NR-IQA模型对黑盒攻击的脆弱性。所提出的攻击方法也为进一步探索NR-IQA鲁棒性提供了有力工具。