Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution

• URL: http://arxiv.org/abs/2411.07449v1
• Date: Tue, 12 Nov 2024 00:20:11 GMT
• Title: Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution
• Authors: Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti,
• Abstract summary: Diffusion models have revolutionized image synthesis, garnering significant research interest in recent years. We study discriminative algorithms operating on diffusion trajectories. Our approach demonstrates the presence of patterns across steps that can be leveraged for classification.
• Score: 29.744990195972587
• License:
• Abstract: 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.

Related papers

• G2D2: Gradient-guided Discrete Diffusion for image inverse problem solving [55.185588994883226]
  This paper presents a novel method for addressing linear inverse problems by leveraging image-generation models based on discrete diffusion as priors. To the best of our knowledge, this is the first approach to use discrete diffusion model-based priors for solving image inverse problems.
  arXiv  Detail & Related papers  (2024-10-09T06:18:25Z)
• Edge-preserving noise for diffusion models [4.435514696080208]
  We present a novel edge-preserving diffusion model that is a generalization of denoising diffusion probablistic models (DDPM) In particular, we introduce an edge-aware noise scheduler that varies between edge-preserving and isotropic Gaussian noise. We show that our model's generative process converges faster to results that more closely match the target distribution.
  arXiv  Detail & Related papers  (2024-10-02T13:29:52Z)
• Generative Diffusion From An Action Principle [0.0]
  We show that score matching can be derived from an action principle, like the ones commonly used in physics. We use this insight to demonstrate the connection between different classes of diffusion models.
  arXiv  Detail & Related papers  (2023-10-06T18:00:00Z)
• Denoising Diffusion Bridge Models [54.87947768074036]
  Diffusion models are powerful generative models that map noise to data using processes. For many applications such as image editing, the model input comes from a distribution that is not random noise. In our work, we propose Denoising Diffusion Bridge Models (DDBMs)
  arXiv  Detail & Related papers  (2023-09-29T03:24:24Z)
• Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution [0.0]
  Current methods assume the degradation to be known and provide impressive results in terms of restoration and diversity. In this work, we leverage the efficiency of those models to jointly estimate the restored image and unknown parameters of the kernel model. Our method alternates between approximating the expected log-likelihood of the problem using samples drawn from a diffusion model and a step to estimate unknown model parameters.
  arXiv  Detail & Related papers  (2023-09-01T06:47:13Z)
• A Geometric Perspective on Diffusion Models [57.27857591493788]
  We inspect the ODE-based sampling of a popular variance-exploding SDE. We establish a theoretical relationship between the optimal ODE-based sampling and the classic mean-shift (mode-seeking) algorithm.
  arXiv  Detail & Related papers  (2023-05-31T15:33:16Z)
• A Variational Perspective on Solving Inverse Problems with Diffusion Models [101.831766524264]
  Inverse tasks can be formulated as inferring a posterior distribution over data. This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. We propose a variational approach that by design seeks to approximate the true posterior distribution.
  arXiv  Detail & Related papers  (2023-05-07T23:00:47Z)
• Reflected Diffusion Models [93.26107023470979]
  We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data. Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
  arXiv  Detail & Related papers  (2023-04-10T17:54:38Z)
• Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later [1.8416014644193066]
  We observe that the reverse diffusion process that underlies image generation has the following properties. Individual trajectories tend to be low-dimensional and resemble 2D rotations' We find that this solution accurately describes the initial phase of image generation for pretrained models.
  arXiv  Detail & Related papers  (2023-03-04T20:08:57Z)
• How Much is Enough? A Study on Diffusion Times in Score-based Generative Models [76.76860707897413]
  Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution. We show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process.
  arXiv  Detail & Related papers  (2022-06-10T15:09:46Z)
• A Variational Perspective on Diffusion-Based Generative Models and Score Matching [8.93483643820767]
  We derive a variational framework for likelihood estimation for continuous-time generative diffusion. We show that minimizing the score-matching loss is equivalent to maximizing a lower bound of the likelihood of the plug-in reverse SDE.
  arXiv  Detail & Related papers  (2021-06-05T05:50:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.