GuardDoor：通过保护性后门防范恶意扩散模型编辑
GuardDoor: Safeguarding Against Malicious Diffusion Editing via Protective Backdoors
AI总结：
本文提出GuardDoor保护机制，通过模型提供商微调图像编码器嵌入保护性后门，使图像所有者附加难以察觉的触发器，导致未经授权的扩散模型编辑产生无意义输出，有效防范恶意编辑且对图像预处理具备稳健性。
摘要：
扩散模型日益普及，彻底改变了图像编辑领域，但也引发了关于未经授权修改（如虚假信息和抄袭）的严重担忧。现有的对策主要依赖于旨在破坏扩散模型输出的对抗性扰动。然而，这些方法很容易被简单的图像预处理技术（如压缩和添加噪声）所中和。为了解决这一局限性，我们提出了GuardDoor，这是一种新颖且稳健的保护机制，可促进图像所有者与模型提供商之间的协作。具体而言，参与该机制的模型提供商会微调图像编码器以嵌入保护性后门，允许图像所有者请求将难以察觉的触发器附加到其图像上。当未经授权的用户尝试使用该扩散模型编辑这些受保护的图像时，模型会产生无意义的输出，从而降低恶意图像编辑的风险。我们的方法展现出了对图像预处理操作的增强稳健性，并且可扩展以进行大规模部署。这项工作强调了模型提供商与图像所有者之间的协作框架在生成式AI时代保护数字内容的潜力。
英文摘要：
The growing accessibility of diffusion models has revolutionized image editing but also raised significant concerns about unauthorized modifications, such as misinformation and plagiarism. Existing countermeasures largely rely on adversarial perturbations designed to disrupt diffusion model outputs. However, these approaches are found to be easily neutralized by simple image preprocessing techniques, such as compression and noise addition. To address this limitation, we propose GuardDoor, a novel and robust protection mechanism that fosters collaboration between image owners and model providers. Specifically, the model provider participating in the mechanism fine-tunes the image encoder to embed a protective backdoor, allowing image owners to request the attachment of imperceptible triggers to their images. When unauthorized users attempt to edit these protected images with this diffusion model, the model produces meaningless outputs, reducing the risk of malicious image editing. Our method demonstrates enhanced robustness against image preprocessing operations and is scalable for large-scale deployment. This work underscores the potential of cooperative frameworks between model providers and image owners to safeguard digital content in the era of generative AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
