Causal perception inspired representation learning for trustworthy image quality assessment
Lei Wang, Qingbo Wu, Desen Yuan, Fanman Meng, Zhengning Wang, King Ngi Ngan · Displays · 2025
Despite great success in modeling visual perception, deep neural network based image quality assessment (IQA) still remains untrustworthy in real-world applications due to its vulnerability to adversarial perturbations. In this paper, we propose to build a trustworthy IQA model via Causal Perception inspired Representation Learning (CPRL). 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. We propose causal intervention to boost CPR and eliminate N-CPR. Specifically, we first generate a series of N-CPR intervention images, and then minimize the causal invariance loss. Then we propose a SortMask module to reduce Lipschitz and improve robustness. SortMask block small changes around the mean to eliminate N-CPR and can be plug-and-play. Experiments on four benchmark databases show that the proposed CPRL method outperforms many state-of-the-art methods and provides explicit model interpretation. To support reproducible scientific research, we release the code at https://clearlovewl.github.io .