Watermarking Autoregressive Image Generation
Watermarking the outputs of generative models has emerged as a promising
approach for tracking their provenance. Despite significant interest in
autoregressive image generation models and their potential for misuse, no prior
work has attempted to watermark their outputs at the token level. In this work,
we present the first such approach by adapting language model watermarking
techniques to this setting. We identify a key challenge: the lack of reverse
cycle-consistency (RCC), wherein re-tokenizing generated image tokens
significantly alters the token sequence, effectively erasing the watermark. To
address this and to make our method robust to common image transformations,
neural compression, and removal attacks, we introduce (i) a custom
tokenizer-detokenizer finetuning procedure that improves RCC, and (ii) a
complementary watermark synchronization layer. As our experiments demonstrate,
our approach enables reliable and robust watermark detection with theoretically
grounded p-values.
6859bc470e4ad7e219758481
Nikola Jovanović
6859bc470e4ad7e219758482
Ismail Labiad
6859bc470e4ad7e219758483
Tomáš Souček
6859bc470e4ad7e219758484
Martin Vechev
6859bc470e4ad7e219758485
Pierre Fernandez
Watermarking Autoregressive Image Generation
Watermarking the outputs of generative models has emerged as a promising
approach for tracking their provenance. Despite significant interest in
autoregressive image generation models and their potential for misuse, no prior
work has attempted to watermark their outputs at the token level. In this work,
we present the first such approach by adapting language model watermarking
techniques to this setting. We identify a key challenge: the lack of reverse
cycle-consistency (RCC), wherein re-tokenizing generated image tokens
significantly alters the token sequence, effectively erasing the watermark. To
address this and to make our method robust to common image transformations,
neural compression, and removal attacks, we introduce (i) a custom
tokenizer-detokenizer finetuning procedure that improves RCC, and (ii) a
complementary watermark synchronization layer. As our experiments demonstrate,
our approach enables reliable and robust watermark detection with theoretically
grounded p-values.
A novel watermarking technique for autoregressive image generation models achieves reliable detection through improved reverse cycle-consistency and synchronization layers.
autoregressive image generation models watermarking token level language model watermarking reverse cycle-consistency tokenizer-detokenizer synchronization layer