title: Robust Watermarking Using Generative Priors Against Image Editing: From
    Benchmarking to Advances
summary: Current image watermarking methods are vulnerable to advanced image editing
  techniques enabled by large-scale text-to-image models. These models can
  distort embedded watermarks during editing, posing significant challenges to
  copyright protection. In this work, we introduce W-Bench, the first
  comprehensive benchmark designed to evaluate the robustness of watermarking
  methods against a wide range of image editing techniques, including image
  regeneration, global editing, local editing, and image-to-video generation.
  Through extensive evaluations of eleven representative watermarking methods
  against prevalent editing techniques, we demonstrate that most methods fail to
  detect watermarks after such edits. To address this limitation, we propose
  VINE, a watermarking method that significantly enhances robustness against
  various image editing techniques while maintaining high image quality. Our
  approach involves two key innovations: (1) we analyze the frequency
  characteristics of image editing and identify that blurring distortions exhibit
  similar frequency properties, which allows us to use them as surrogate attacks
  during training to bolster watermark robustness; (2) we leverage a large-scale
  pretrained diffusion model SDXL-Turbo, adapting it for the watermarking task to
  achieve more imperceptible and robust watermark embedding. Experimental results
  show that our method achieves outstanding watermarking performance under
  various image editing techniques, outperforming existing methods in both image
  quality and robustness. Code is available at https://github.com/Shilin-LU/VINE.
paper_authors._id: 671aed8907e37a9b52c3f7d1
paper_authors.name: Shilin Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-25T09:30:31.701Z
paper_authors._id: 631c4a23aa346997917bcb89
paper_authors.avatarUrl: /avatars/4a562ba78e6be289049027c27798cc6e.svg
paper_authors.fullname: Shilin Lu
paper_authors.type: user
paper_authors.user: Shilin-LU
paper_authors._id: 671aed8907e37a9b52c3f7d2
paper_authors.name: Zihan Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-31T09:55:15.113Z
paper_authors._id: 66923522e32997bdf7f37462
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-RE4FqyZnDF9PcpY6ZBGH.png
paper_authors.fullname: Zihan Zhou
paper_authors.type: user
paper_authors.user: Edennnnn
paper_authors._id: 671aed8907e37a9b52c3f7d3
paper_authors.name: Jiayou Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-25T11:49:26.030Z
paper_authors._id: 61adb088bf0f6d4547f402f4
paper_authors.avatarUrl: /avatars/68bcf083765aadfa348745fcc2b35545.svg
paper_authors.fullname: Jiayou Lu
paper_authors.type: user
paper_authors.user: devSulyvahn
paper_authors._id: 671aed8907e37a9b52c3f7d4
paper_authors.name: Yuanzhi Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-25T11:49:19.721Z
paper_authors._id: 627d2723401f42c57b6b7c0c
paper_authors.avatarUrl: /avatars/6ff754e56aaee63d8572881a6a966171.svg
paper_authors.fullname: Yuanzhi Zhu
paper_authors.type: user
paper_authors.user: Yuanzhi
paper_authors._id: 671aed8907e37a9b52c3f7d5
paper_authors.name: Adams Wai-Kin Kong
paper_title: Robust Watermarking Using Generative Priors Against Image Editing: From
    Benchmarking to Advances
paper_summary: Current image watermarking methods are vulnerable to advanced image editing
  techniques enabled by large-scale text-to-image models. These models can
  distort embedded watermarks during editing, posing significant challenges to
  copyright protection. In this work, we introduce W-Bench, the first
  comprehensive benchmark designed to evaluate the robustness of watermarking
  methods against a wide range of image editing techniques, including image
  regeneration, global editing, local editing, and image-to-video generation.
  Through extensive evaluations of eleven representative watermarking methods
  against prevalent editing techniques, we demonstrate that most methods fail to
  detect watermarks after such edits. To address this limitation, we propose
  VINE, a watermarking method that significantly enhances robustness against
  various image editing techniques while maintaining high image quality. Our
  approach involves two key innovations: (1) we analyze the frequency
  characteristics of image editing and identify that blurring distortions exhibit
  similar frequency properties, which allows us to use them as surrogate attacks
  during training to bolster watermark robustness; (2) we leverage a large-scale
  pretrained diffusion model SDXL-Turbo, adapting it for the watermarking task to
  achieve more imperceptible and robust watermark embedding. Experimental results
  show that our method achieves outstanding watermarking performance under
  various image editing techniques, outperforming existing methods in both image
  quality and robustness. Code is available at https://github.com/Shilin-LU/VINE.
paper_ai_summary: A benchmark evaluates watermark robustness against image editing, and VINE proposes an enhanced watermarking method using diffusion models for improved robustness and image quality.
paper_ai_keywords: watermarking W-Bench VINE image editing image regeneration global editing local editing image-to-video generation frequency characteristics blurring distortions diffusion model SDXL-Turbo watermark embedding watermark performance