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.

翻译：近年来，扩散模型已成为图像合成领域的先进方法。基于扩散模型的编辑模型以其高保真度和精确性著称，却意外引发了图像版权侵权与恶意编辑等新挑战。本研究首次系统性地定义并解决该问题。在评估并尝试增强传统图像水印技术后，我们认识到其在新兴应用场景中的局限性。为此，我们提出一种名为RIW（鲁棒不可见水印）的新技术，利用对抗样本方法嵌入不可见水印。该技术确保编辑后不可见水印的提取准确率达96%，而传统方法仅为0%。相关代码已开源至https://github.com/BennyTMT/RIW。