D3QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection

The emergence of visual autoregressive (AR) models has revolutionized image generation, while presenting new challenges for synthetic image detection.Unlike previous GAN or diffusion-based methods, AR models generate images through discrete token prediction, exhibiting both marked improvements in image synthesis quality and unique characteristics in their vector-quantized representations. In this paper, we propose to leverage Discrete Distribution Discrepancy-aware Quantization Error ($\bf{D^3QE}$) for autoregressive-generated image detection that exploits the distinctive patterns and the frequency distribution bias of the codebook existing in real and fake images. We introduce a discrete distribution discrepancy-aware transformer that integrates dynamic codebook frequency statistics into its attention mechanism, fusing semantic features and quantization error latent.To evaluate our method, we construct a comprehensive dataset covering 7 mainstream visual AR models.Experiments demonstrate superior detection accuracy and strong generalization of $\bf{D^3QE}$ across different AR models, while maintaining robustness under various real-world perturbations.