  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.
