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 under black-box setting, 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
this https URL

通过黑盒攻击评估非参考视频质量评估模型的鲁棒性。