Autoregressive Images Watermarking through Lexical Biasing: An Approach
  Resistant to Regeneration Attack
Authors: name: Siqi Hui, name: Yiren Song, name: Sanping Zhou, name: Ye Deng, name: Wenli Huang, name: Jinjun Wang

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.