Abstract
Recently, the area of adversarial attacks on image quality metrics has begun to be explored, whereas the area of defences remains under-researched. In this study, we aim to cover that case and check the transferability of adversarial purification defences from image classifiers to IQA methods. In this paper, we apply several widespread attacks on IQA models and examine the success of the defences against them. The purification methodologies covered different preprocessing techniques, including geometrical transformations, compression, denoising, and modern neural network-based methods. Also, we address the challenge of assessing the efficacy of a defensive methodology by proposing ways to estimate output visual quality and the success of neutralizing attacks. Defences were tested against attack on three IQA metrics -- Linearity, MetaIQA and SPAQ. The code for attacks and defences is available at: (link is hidden for a blind review).
Abstract (translated)
近年来，针对图像质量度量的对抗攻击领域开始受到关注，而防御领域的研究仍较为不足。在这项研究中，我们将探讨这一情况，并检查图像分类器用于IQA方法中的对抗净化防御的传输性。在本文中，我们针对多个广泛使用的攻击对IQA模型进行了研究，并检查了它们对攻击的防御效果。所涵盖的净化方法包括几何变换、压缩、去噪和基于现代神经网络的方法。此外，我们还提出了估计输出视觉质量和抵消攻击成功性的方法，以评估防御策略的有效性。防御措施针对三个IQA指标——线性ity，元IQA和SPAQ进行了测试。攻击和防御代码可在此处查看：（链接被隐藏以进行盲评）。
URL
https://arxiv.org/abs/2404.06957
