Towards Extensible Detection of AI-Generated Images via Content-Agnostic Adapter-Based Category-Aware Incremental Learning

The rapid evolution of image generation techniques has benefited several fields, but it has also given rise to security concerns. As countermeasures, a series of AI-generated image detection methods have been developed successfully. However, existing methods exhibit an inefficiency in handling the continual emergence of new generative models. To address this issue, we formulate the detection of AI-generated images in an extensible manner using an adapter-based domain incremental learning framework. Specifically, we first investigate the global consistency property of generation artifacts and design a content-agnostic adapter equipped on a vision transformer to extract common forensic features, where a token-level shuffling strategy is constructed for the dual-stream comparison to mitigate the fitting to specific image content. Then, motivated by the compactness of real images and the diversity of fake images due to their inherent generation processes, an asymmetric category-aware domain alignment method is designed to reduce the domain shift arisen from different generators. Finally, a multi-view knowledge distillation module, considering both point-to-point and structure-to-structure forensic knowledge, is devised to alleviate catastrophic forgetting. Experiments are conducted on several protocols using various image generators, and experimental results verify the superiority of our method compared to state-of-the-art methods for extensible detection.