title: Training-Free Watermarking for Autoregressive Image Generation
summary: 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.
paper_authors._id: 682d713e6c66e25ab59fc800
paper_authors.name: Yu Tong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:40:01.048Z
paper_authors._id: 65c38108425a226a29f00365
paper_authors.avatarUrl: /avatars/c276a0d2b108df446246c31a396496f0.svg
paper_authors.fullname: tongyu
paper_authors.type: user
paper_authors.user: yutchina02
paper_authors._id: 682d713e6c66e25ab59fc801
paper_authors.name: Zihao Pan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:39:58.888Z
paper_authors._id: 65ad57da57f263e3d030187a
paper_authors.avatarUrl: /avatars/e1e3c4119180aee5fb660d1e5f745e99.svg
paper_authors.fullname: 潘子豪
paper_authors.type: user
paper_authors.user: Apostle723
paper_authors._id: 682d713e6c66e25ab59fc802
paper_authors.name: Shuai Yang
paper_authors._id: 682d713e6c66e25ab59fc803
paper_authors.name: Kaiyang Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:20:44.575Z
paper_authors._id: 62ac6656de8bfbb93094b8fd
paper_authors.avatarUrl: /avatars/1d8f445b6d5f9d43ddab81056bcb141e.svg
paper_authors.fullname: Kaiyang Zhou
paper_authors.type: user
paper_authors.user: kaiyangzhou
paper_title: Training-Free Watermarking for Autoregressive Image Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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