Training-Free Watermarking for Autoregressive Image Generation
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.
682d713e6c66e25ab59fc800
Yu Tong
claimed_verified
2025-05-21T08:40:01.048Z
65c38108425a226a29f00365
/avatars/c276a0d2b108df446246c31a396496f0.svg
tongyu
user
yutchina02
682d713e6c66e25ab59fc801
Zihao Pan
claimed_verified
2025-05-21T08:39:58.888Z
65ad57da57f263e3d030187a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dJ3DYSIlv3Pb_6IEbqwOQ.png
潘子豪
user
Apostle723
682d713e6c66e25ab59fc802
Shuai Yang
682d713e6c66e25ab59fc803
Kaiyang Zhou
admin_assigned
2025-05-21T10:20:44.575Z
62ac6656de8bfbb93094b8fd
/avatars/1d8f445b6d5f9d43ddab81056bcb141e.svg
Kaiyang Zhou
user
kaiyangzhou
Training-Free Watermarking for Autoregressive Image Generation
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.
IndexMark is a training-free watermarking framework for autoregressive image generation models that embeds watermarks by replacing generated indices with similar ones, maintaining image quality and robustness against various perturbations.
autoregressive image generation models IndexMark codebook token similarity watermark tokens token replacement Index Encoder cropping attacks verification accuracy Gaussian blur random erasing color jittering JPEG compression