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).

翻译：近期，图像质量指标的对抗攻击领域开始受到探索，而防御领域仍研究不足。本研究旨在填补这一空白，检验对抗净化防御从图像分类器迁移至图像质量评估方法的可行性。本文对图像质量评估模型实施了多种常见攻击，并考察了防御方法的成功率。所涉及的净化方法涵盖不同预处理技术，包括几何变换、压缩、去噪以及现代基于神经网络的方法。此外，我们通过提出评估输出视觉质量和中和攻击成功率的方法，解决了防御方法论有效性评估的挑战。防御方法针对三种图像质量指标——Linearity、MetaIQA和SPAQ的攻击进行了测试。攻击与防御代码可于以下链接获取（链接已隐藏以进行盲审）。