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
this https URL

最近，扩散模型（DMs）已成为图像合成的最先进方法，而基于 DMs 的编辑模型，以其高保真度和精度而闻名，无意中引入了与图像版权侵权和恶意编辑相关的新挑战。我们的工作是首次对这个问题进行规范化和解决。经过评估和试图增强传统图像水印技术后，我们意识到它们在这个新兴环境中的局限性。为此，我们开发了一种新颖的技术，RIW（稳健隐形水印），利用对抗示例技术嵌入隐形水印。与传统方法提供的 0% 相比，我们的技术确保了在编辑后的隐形水印的 96% 的高提取准确性。我们提供了我们的代码访问链接。

一种相对强健的基于扩散编辑模型的图像水印

A Somewhat Robust Image Watermark against Diffusion-based Editing Models

Data hiding such as steganography and invisible watermarking has important
applications in copyright protection, privacy-preserved communication and
content provenance. Existing works often fall short in either preserving image
quality, or robustness against perturbations or are too complex to train. We
propose RoSteALS, a practical steganography technique leveraging frozen
pretrained autoencoders to free the payload embedding from learning the
distribution of cover images. RoSteALS has a light-weight secret encoder of
just 300k parameters, is easy to train, has perfect secret recovery performance
and comparable image quality on three benchmarks. Additionally, RoSteALS can be
adapted for novel cover-less steganography applications in which the cover
image can be sampled from noise or conditioned on text prompts via a denoising
diffusion process. Our model and code are available at
https://github.com/TuBui/RoSteALS.

提出 RoSteALS 一种轻量级的隐写术技术，使用预训练自编码器来解放载荷嵌入，并且在三个基准上具有完美的秘密恢复性能和可比较的图像质量。