Training-Free Watermarking for Autoregressive Image Generation
Authors: name: Yu Tong, name: Zihao Pan, name: Shuai Yang, name: Kaiyang Zhou
Organization: Hong Kong Baptist University

Invisible image watermarking can protect image ownership and prevent
malicious misuse of visual generative models. However, existing generative
watermarking methods are mainly designed for diffusion models while
watermarking for autoregressive image generation models remains largely
underexplored. We propose IndexMark, a training-free watermarking framework for
autoregressive image generation models. IndexMark is inspired by the redundancy
property of the codebook: replacing autoregressively generated indices with
similar indices produces negligible visual differences. The core component in
IndexMark is a simple yet effective match-then-replace method, which carefully
selects watermark tokens from the codebook based on token similarity, and
promotes the use of watermark tokens through token replacement, thereby
embedding the watermark without affecting the image quality. Watermark
verification is achieved by calculating the proportion of watermark tokens in
generated images, with precision further improved by an Index Encoder.
Furthermore, we introduce an auxiliary validation scheme to enhance robustness
against cropping attacks. Experiments demonstrate that IndexMark achieves
state-of-the-art performance in terms of image quality and verification
accuracy, and exhibits robustness against various perturbations, including
cropping, noises, Gaussian blur, random erasing, color jittering, and JPEG
compression.