A Somewhat Robust Image Watermark against Diffusion-based Editing Models
- URL: http://arxiv.org/abs/2311.13713v2
- Date: Thu, 7 Dec 2023 19:17:11 GMT
- Title: A Somewhat Robust Image Watermark against Diffusion-based Editing Models
- Authors: Mingtian Tan, Tianhao Wang, Somesh Jha
- Abstract summary: Editing models based on diffusion models (DMs) have inadvertently introduced new challenges related to image copyright infringement and malicious editing.
We develop a novel technique, RIW (Robust Invisible Watermarking), to embed invisible watermarks.
Our technique ensures a high extraction accuracy of $96%$ for the invisible watermark after editing, compared to the $0%$ offered by conventional methods.
- Score: 25.034612051522167
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, diffusion models (DMs) have become the state-of-the-art method for
image synthesis. Editing models based on DMs, known for their high fidelity and
precision, have inadvertently introduced new challenges related to image
copyright infringement and malicious editing. Our work is the first to
formalize and address this issue. After assessing and attempting to enhance
traditional image watermarking techniques, we recognize their limitations in
this emerging context. In response, we develop a novel technique, RIW (Robust
Invisible Watermarking), to embed invisible watermarks leveraging adversarial
example techniques. Our technique ensures a high extraction accuracy of $96\%$
for the invisible watermark after editing, compared to the $0\%$ offered by
conventional methods. We provide access to our code at
https://github.com/BennyTMT/RIW.
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