Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property (IP) infringement associated with these models. Therefore, it is necessary to analyze the origin of images by inferring if a specific image was generated by a particular model, i.e., origin attribution. Existing methods are limited in their applicability to specific types of generative models and require additional steps during training or generation. This restricts their use with pre-trained models that lack these specific operations and may compromise the quality of image generation. To overcome this problem, we first develop an alteration-free and model-agnostic origin attribution method via input reverse-engineering on image generation models, i.e., inverting the input of a particular model for a specific image. Given a particular model, we first analyze the differences in the hardness of reverse-engineering tasks for the generated images of the given model and other images. Based on our analysis, we propose a method that utilizes the reconstruction loss of reverse-engineering to infer the origin. Our proposed method effectively distinguishes between generated images from a specific generative model and other images, including those generated by different models and real images.

翻译：近年来，图像生成模型备受关注。然而，这些模型可能引发滥用和知识产权侵权问题，因此有必要通过推断特定图像是否由某模型生成来分析其来源，即来源归属。现有方法仅适用于特定类型的生成模型，且需要在训练或生成过程中增加额外步骤，这限制了其在缺乏相关操作的预训练模型中的应用，并可能损害图像生成质量。为解决此问题，我们首次提出一种无需修改且无关模型的来源归属方法，该方法通过对图像生成模型进行输入逆向工程（即针对特定图像反推特定模型的输入）实现。针对给定模型，我们首先分析该模型生成图像与其他图像在逆向工程任务难度上的差异。基于此分析，我们提出利用逆向工程的重建损失来推断来源。我们的方法能有效区分特定生成模型的输出图像与其他图像（包括不同模型生成的图像和真实图像）。