Despite great success in modeling visual perception, deep neural network based image quality assessment (IQA) still remains unreliable in real-world applications due to its vulnerability to adversarial perturbations and the inexplicit black-box structure. In this paper, we propose to build a trustworthy IQA model via Causal Perception inspired Representation Learning (CPRL), and a score reflection attack method for IQA model. More specifically, we assume that each image is composed of Causal Perception Representation (CPR) and non-causal perception representation (N-CPR). CPR serves as the causation of the subjective quality label, which is invariant to the imperceptible adversarial perturbations. Inversely, N-CPR presents spurious associations with the subjective quality label, which may significantly change with the adversarial perturbations. To extract the CPR from each input image, we develop a soft ranking based channel-wise activation function to mediate the causally sufficient (beneficial for high prediction accuracy) and necessary (beneficial for high robustness) deep features, and based on intervention employ minimax game to optimize. Experiments on four benchmark databases show that the proposed CPRL method outperforms many state-of-the-art adversarial defense methods and provides explicit model interpretation.

翻译：尽管基于深度神经网络的图像质量评估(IQA)在模拟视觉感知方面取得了巨大成功，但由于其易受对抗性扰动影响且存在不透明的黑箱结构，在真实应用中仍不可靠。本文提出通过因果感知启发的表示学习(CPRL)构建可信IQA模型，并设计针对IQA模型的分数反射攻击方法。具体而言，我们假设每幅图像由因果感知表示(CPR)与非因果感知表示(N-CPR)组成。CPR作为主观质量标签的因果因素，对不可察觉的对抗扰动具有不变性；相反，N-CPR呈现出与主观质量标签的虚假关联，会随对抗扰动发生显著变化。为从每幅输入图像中提取CPR，我们开发了一种基于软排名的通道级激活函数，用以调解因果充分性（有利于高预测精度）与因果必要性（有利于高鲁棒性）的深度特征，并通过干预手段采用极小极大博弈进行优化。在四个基准数据库上的实验表明，所提出的CPRL方法优于多种先进对抗防御方法，并提供了明确的模型可解释性。