Causal Representation Learning for GAN-Generated Face Image Quality Assessment

Recent years have witnessed significant advancements in face image generation using generative adversarial networks (GANs), leading to a high demand for GAN-generated face image quality assessment (GFIQA). However, the intrinsic distortion caused by the generation brings a significant challenge for existing image quality assessment (IQA) models which are typically designed for natural images. In addition, the image distortion usually varies depending on different GAN models, resulting in a high generalization capability that a GFIQA model should possess. To account for this, we first establish a large GFIQA database by collecting various GFIs from existing popular GAN models. Subsequently, we further propose a causal representation learning (CRL) scheme for the generalized GFIQA model (CRL-GFIQA) with the assumption that the causal knowledge of human quality assessment is shareable in different scenarios. In particular, we disentangle the learned features into casual and non-causal components by an invertible neural network, facilitating the proposed CRL-GFIQA model with a high generalization on unseen domains. Extensive experimental results demonstrate the effectiveness of our CRL-GFIQA model. The codes and the constructed dataset will be publicly available.