551 research outputs found
Oscillatory behavior of delay partial difference equations with positive and negative coefficients
AbstractWe construct an important transform to obtain sufficient conditions for the oscillation of all solutions of delay partial difference equations with positive and negative coefficients of the form Am+1,n + Am,n+1 − Amn + pmnAm−k−1 − qmnAm−k′,n−l′ = 0, where m, n = 0, 1, …, and k, k′, l′, l are nonnegative integers, p, q ϵ (0, ∞), the coefficients {qmn} and {pmn} are sequences of nonnegative real numbers
Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
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 and 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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