Watermarking Autoregressive Image Generation
Authors: name: Nikola Jovanović, name: Ismail Labiad, name: Tomáš Souček, name: Martin Vechev, name: Pierre Fernandez

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.