Robust Watermarking Using Generative Priors Against Image Editing: From
  Benchmarking to Advances
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.
671aed8907e37a9b52c3f7d1
Shilin Lu
claimed_verified
2024-10-25T09:30:31.701Z
631c4a23aa346997917bcb89
/avatars/4a562ba78e6be289049027c27798cc6e.svg
Shilin Lu
user
Shilin-LU
671aed8907e37a9b52c3f7d2
Zihan Zhou
claimed_verified
2024-10-31T09:55:15.113Z
66923522e32997bdf7f37462
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-RE4FqyZnDF9PcpY6ZBGH.png
Zihan Zhou
user
Edennnnn
671aed8907e37a9b52c3f7d3
Jiayou Lu
admin_assigned
2024-10-25T11:49:26.030Z
61adb088bf0f6d4547f402f4
/avatars/68bcf083765aadfa348745fcc2b35545.svg
Jiayou Lu
user
devSulyvahn
671aed8907e37a9b52c3f7d4
Yuanzhi Zhu
admin_assigned
2024-10-25T11:49:19.721Z
627d2723401f42c57b6b7c0c
/avatars/6ff754e56aaee63d8572881a6a966171.svg
Yuanzhi Zhu
user
Yuanzhi
671aed8907e37a9b52c3f7d5
Adams Wai-Kin Kong
Robust Watermarking Using Generative Priors Against Image Editing: From
  Benchmarking to Advances
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.
A benchmark evaluates watermark robustness against image editing, and VINE proposes an enhanced watermarking method using diffusion models for improved robustness and image quality.
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