Autoregressive Images Watermarking through Lexical Biasing: An Approach
  Resistant to Regeneration Attack
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.
6842746e8edd398d01b68e03
Siqi Hui
6842746e8edd398d01b68e04
Yiren Song
6842746e8edd398d01b68e05
Sanping Zhou
6842746e8edd398d01b68e06
Ye Deng
6842746e8edd398d01b68e07
Wenli Huang
6842746e8edd398d01b68e08
Jinjun Wang
Autoregressive Images Watermarking through Lexical Biasing: An Approach
  Resistant to Regeneration Attack
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.
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.
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