Diffusion models have revolutionized image synthesis, garnering significant research interest in recent years. Diffusion is an iterative algorithm in which samples are generated step-by-step, starting from pure noise. This process introduces the notion of diffusion trajectories, i.e., paths from the standard Gaussian distribution to the target image distribution. In this context, we study discriminative algorithms operating on these trajectories. Specifically, given a pre-trained diffusion model, we consider the problem of classifying images as part of the training dataset, generated by the model or originating from an external source. Our approach demonstrates the presence of patterns across steps that can be leveraged for classification. We also conduct ablation studies, which reveal that using higher-order gradient features to characterize the trajectories leads to significant performance gains and more robust algorithms.

本文解决了图像合成中的一个关键问题，即如何识别生成图像的来源。通过对扩散轨迹的深入研究，提出了一种新颖的分类方法，利用逐步生成过程中的模式，实现图像的有效分类。研究结果表明，使用高阶梯度特征来表征轨迹可以显著提高性能，促进更稳健的算法发展。

追根溯源：利用扩散轨迹中的时间动态进行来源归属