HRR: Hierarchical Retrospection Refinement for Generated Image Detection
This addresses the challenge of generative image detection for security and authenticity applications, but it is incremental as it builds on existing detection methods with novel modules.
The paper tackles the problem of detecting whether an image is real or AI-generated by proposing the HRR framework, which uses a multi-scale style retrospection module and a feature refinement module to improve generalization, achieving significant performance improvements over state-of-the-art methods.
Generative artificial intelligence holds significant potential for abuse, and generative image detection has become a key focus of research. However, existing methods primarily focused on detecting a specific generative model and emphasizing the localization of synthetic regions, while neglecting the interference caused by image size and style on model learning. Our goal is to reach a fundamental conclusion: Is the image real or generated? To this end, we propose a diffusion model-based generative image detection framework termed Hierarchical Retrospection Refinement~(HRR). It designs a multi-scale style retrospection module that encourages the model to generate detailed and realistic multi-scale representations, while alleviating the learning biases introduced by dataset styles and generative models. Additionally, based on the principle of correntropy sparse additive machine, a feature refinement module is designed to reduce the impact of redundant features on learning and capture the intrinsic structure and patterns of the data, thereby improving the model's generalization ability. Extensive experiments demonstrate the HRR framework consistently delivers significant performance improvements, outperforming state-of-the-art methods in generated image detection task.