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 \emph{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 attack method outperforms all compared state-of-the-art methods and is far
ahead of previous black-box methods. The effective DBCNN model suffers a
Spearman rank-order correlation coefficient (SROCC) decline of $0.6972$
attacked by our method, revealing the vulnerability of NR-IQA to black-box
attacks. The proposed attack method also provides a potent tool for further
exploration into NR-IQA robustness.

本文提出了一种基于查询的黑盒攻击方法，通过使用多个分数边界和人类视觉系统的特性，使得攻击方法能够有效地测试无参考图像质量评估方法的稳健性。实验证明，该攻击方法优于其他先进方法，并揭示了 NR-IQA 在黑盒攻击下的脆弱性，同时为进一步研究 NR-IQA 的稳健性提供了有力工具。