Robust Watermarking Using Generative Priors Against Image Editing: From
  Benchmarking to Advances
Authors: name: Shilin Lu, name: Zihan Zhou, name: Jiayou Lu, name: Yuanzhi Zhu, name: Adams Wai-Kin Kong

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.