  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.
