Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method

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 di...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Yang, Chenxi, Liu, Yujia, Li, Dingquan, jiang, Tingting
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.01.2024
Subjects
Black boxes
Correlation coefficients
Image processing
Image quality
Iterative methods
Methods
Quality assessment
Robustness
User experience
Visual tasks
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422