Abstract
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.
Abstract (translated)
尽管在建模视觉感知方面取得了巨大的成功，但基于深度神经网络的图像质量评估（IQA）仍然不可靠，因为在实际应用中容易受到对抗扰动的影响，并且具有难以解释的黑盒结构。在本文中，我们提出了一种通过Causal Perception启发下的表示学习（CPRL）构建可靠IQA模型的方法，以及一种IQA模型得分反射攻击方法。具体来说，我们假设每个图像由Causal Perception表示（CPR）和非对称感知表示（N-CPR）组成。CPR作为主观质量标签的因果关系，对不可感知的主观扰动具有不变性。相反，N-CPR表现出与主观质量标签的伪相关性，随着对抗扰动的变化，可能会显著改变。为了从每个输入图像中提取CPR，我们基于通道的激活函数开发了一种软排名方法，以介导足够因果（提高预测准确性）和必要（提高稳健性）的深度特征，并且通过干预采用最小最大游戏进行优化。在四个基准数据库上的实验表明，与最先进的对抗防御方法相比，所提出的CPRL方法具有更好的性能，并提供了明确的模型解释。
URL
https://arxiv.org/abs/2404.19567
