D^3QE: Learning Discrete Distribution Discrepancy-aware
  Quantization Error for Autoregressive-Generated Image Detection
Authors: name: Yanran Zhang, name: Bingyao Yu, name: Yu Zheng, name: Wenzhao Zheng, name: Yueqi Duan, name: Lei Chen, name: Jie Zhou, name: Jiwen Lu

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 (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 termed ARForensics covering 7 mainstream
visual AR models. Experiments demonstrate superior detection accuracy and
strong generalization of D^3QE across different AR models, with robustness to
real-world perturbations. Code is available at
https://github.com/Zhangyr2022/D3QE{https://github.com/Zhangyr2022/D3QE}.