  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.
