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：近期，扩散模型已成为图像合成领域的最新技术。基于扩散模型的编辑模型以其高保真度和高精度著称，却无意中引发了图像版权侵权和恶意编辑等新挑战。本文首次对该问题进行系统定义与应对。在评估并尝试改进传统图像水印技术后，我们认识到其在新兴场景下的局限性。为此，我们提出了一种新方法——RIW（鲁棒隐形水印），利用对抗样本技术嵌入隐形水印。该方法确保编辑后隐形水印的提取准确率达96%，而传统方法的准确率为0%。我们已在 https://github.com/BennyTMT/RIW 提供代码访问。