D^3QE: 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 (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}.
68e663cd975ac4c405ef228a
Yanran Zhang
claimed_verified
2025-10-13T10:10:21.548Z
661cfae9a853782abad2a495
/avatars/39723a07bf9efed8278e009fe966d044.svg
Yanran Zhang
user
Yanran21
68e663cd975ac4c405ef228b
Bingyao Yu
68e663cd975ac4c405ef228c
Yu Zheng
68e663cd975ac4c405ef228d
Wenzhao Zheng
68e663cd975ac4c405ef228e
Yueqi Duan
68e663cd975ac4c405ef228f
Lei Chen
68e663cd975ac4c405ef2290
Jie Zhou
68e663cd975ac4c405ef2291
Jiwen Lu
D^3QE: 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 (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}.
A novel method using Discrete Distribution Discrepancy-aware Quantization Error (D$^3$QE) detects images generated by visual autoregressive models by analyzing codebook frequency statistics and quantization errors.
visual autoregressive (AR) models image generation synthetic image detection discrete token prediction vector-quantized representations Discrete Distribution Discrepancy-aware Quantization Error (D$^3$QE) discrete distribution discrepancy-aware transformer attention mechanism semantic features quantization error latent ARForensics dataset