Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks

• URL: http://arxiv.org/abs/2309.13609v3
• Date: Fri, 20 Oct 2023 10:12:41 GMT
• Title: Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
• Authors: Ao-Xiang Zhang, Yu Ran, Weixuan Tang, Yuan-Gen Wang
• Abstract summary: No-Reference Video Quality Assessment (NR-VQA) plays an essential role in improving the viewing experience of end-users. Recent NR-VQA models based on CNNs and Transformers have achieved outstanding performance. We make the first attempt to evaluate the robustness of NR-VQA models against adversarial attacks.
• Score: 15.127749101160672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: No-Reference Video Quality Assessment (NR-VQA) plays an essential role in improving the viewing experience of end-users. Driven by deep learning, recent NR-VQA models based on Convolutional Neural Networks (CNNs) and Transformers have achieved outstanding performance. To build a reliable and practical assessment system, it is of great necessity to evaluate their robustness. However, such issue has received little attention in the academic community. In this paper, we make the first attempt to evaluate the robustness of NR-VQA models against adversarial attacks, and propose a patch-based random search method for black-box attack. Specifically, considering both the attack effect on quality score and the visual quality of adversarial video, the attack problem is formulated as misleading the estimated quality score under the constraint of just-noticeable difference (JND). Built upon such formulation, a novel loss function called Score-Reversed Boundary Loss is designed to push the adversarial video's estimated quality score far away from its ground-truth score towards a specific boundary, and the JND constraint is modeled as a strict $L_2$ and $L_\infty$ norm restriction. By this means, both white-box and black-box attacks can be launched in an effective and imperceptible manner. The source code is available at https://github.com/GZHU-DVL/AttackVQA.

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