title: Autoregressive Images Watermarking through Lexical Biasing: An Approach
    Resistant to Regeneration Attack
summary: Autoregressive (AR) image generation models have gained increasing attention
  for their breakthroughs in synthesis quality, highlighting the need for robust
  watermarking to prevent misuse. However, existing in-generation watermarking
  techniques are primarily designed for diffusion models, where watermarks are
  embedded within diffusion latent states. This design poses significant
  challenges for direct adaptation to AR models, which generate images
  sequentially through token prediction. Moreover, diffusion-based regeneration
  attacks can effectively erase such watermarks by perturbing diffusion latent
  states. To address these challenges, we propose Lexical Bias Watermarking
  (LBW), a novel framework designed for AR models that resists regeneration
  attacks. LBW embeds watermarks directly into token maps by biasing token
  selection toward a predefined green list during generation. This approach
  ensures seamless integration with existing AR models and extends naturally to
  post-hoc watermarking. To increase the security against white-box attacks,
  instead of using a single green list, the green list for each image is randomly
  sampled from a pool of green lists. Watermark detection is performed via
  quantization and statistical analysis of the token distribution. Extensive
  experiments demonstrate that LBW achieves superior watermark robustness,
  particularly in resisting regeneration attacks.
paper_authors._id: 6842746e8edd398d01b68e03
paper_authors.name: Siqi Hui
paper_authors._id: 6842746e8edd398d01b68e04
paper_authors.name: Yiren Song
paper_authors._id: 6842746e8edd398d01b68e05
paper_authors.name: Sanping Zhou
paper_authors._id: 6842746e8edd398d01b68e06
paper_authors.name: Ye Deng
paper_authors._id: 6842746e8edd398d01b68e07
paper_authors.name: Wenli Huang
paper_authors._id: 6842746e8edd398d01b68e08
paper_authors.name: Jinjun Wang
paper_title: Autoregressive Images Watermarking through Lexical Biasing: An Approach
    Resistant to Regeneration Attack
paper_summary: Autoregressive (AR) image generation models have gained increasing attention
  for their breakthroughs in synthesis quality, highlighting the need for robust
  watermarking to prevent misuse. However, existing in-generation watermarking
  techniques are primarily designed for diffusion models, where watermarks are
  embedded within diffusion latent states. This design poses significant
  challenges for direct adaptation to AR models, which generate images
  sequentially through token prediction. Moreover, diffusion-based regeneration
  attacks can effectively erase such watermarks by perturbing diffusion latent
  states. To address these challenges, we propose Lexical Bias Watermarking
  (LBW), a novel framework designed for AR models that resists regeneration
  attacks. LBW embeds watermarks directly into token maps by biasing token
  selection toward a predefined green list during generation. This approach
  ensures seamless integration with existing AR models and extends naturally to
  post-hoc watermarking. To increase the security against white-box attacks,
  instead of using a single green list, the green list for each image is randomly
  sampled from a pool of green lists. Watermark detection is performed via
  quantization and statistical analysis of the token distribution. Extensive
  experiments demonstrate that LBW achieves superior watermark robustness,
  particularly in resisting regeneration attacks.
paper_ai_summary: A novel watermarking technique, Lexical Bias Watermarking, enhances the security of autoregressive image generation models by embedding watermarks into token selection, demonstrating superior resistance to regeneration attacks.
paper_ai_keywords: autoregressive models in-generation watermarking diffusion models diffusion latent states token prediction regeneration attacks Lexical Bias Watermarking token maps green list watermark detection quantization statistical analysis token distribution