Related papers: FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error

FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error

URL: http://arxiv.org/abs/2412.07140v1
Date: Tue, 10 Dec 2024 03:02:34 GMT
Title: FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error
Authors: Beilin Chu, Xuan Xu, Xin Wang, Yufei Zhang, Weike You, Linna Zhou,
Abstract summary: diffusion models struggle to accurately reconstruct mid-band frequency information in real images. Fire-guided Reconstruction Error is first to investigate influence of frequency decomposition on reconstruction error. Experiments show that FIRE generalizes effectively to unseen diffusion models and maintains robustness against diverse perturbations.
Score: 12.888629653445012
License:
Abstract: The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real images and raising concerns about potential misuse. In this paper, we observe that diffusion models struggle to accurately reconstruct mid-band frequency information in real images, suggesting the limitation could serve as a cue for detecting diffusion model generated images. Motivated by this observation, we propose a novel method called Frequency-guided Reconstruction Error (FIRE), which, to the best of our knowledge, is the first to investigate the influence of frequency decomposition on reconstruction error. FIRE assesses the variation in reconstruction error before and after the frequency decomposition, offering a robust method for identifying diffusion model generated images. Extensive experiments show that FIRE generalizes effectively to unseen diffusion models and maintains robustness against diverse perturbations.

Related papers

Lossy Image Compression with Foundation Diffusion Models [10.407650300093923]
In this work we formulate the removal of quantization error as a denoising task, using diffusion to recover lost information in the transmitted image latent. Our approach allows us to perform less than 10% of the full diffusion generative process and requires no architectural changes to the diffusion model.
arXiv Detail & Related papers (2024-04-12T16:23:42Z)
ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
arXiv Detail & Related papers (2024-03-21T17:52:08Z)
TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method [2.626378252978696]
We propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model.
arXiv Detail & Related papers (2024-02-17T13:09:00Z)
Global Structure-Aware Diffusion Process for Low-Light Image Enhancement [64.69154776202694]
This paper studies a diffusion-based framework to address the low-light image enhancement problem. We advocate for the regularization of its inherent ODE-trajectory. Experimental evaluations reveal that the proposed framework attains distinguished performance in low-light enhancement.
arXiv Detail & Related papers (2023-10-26T17:01:52Z)
Reconstruct-and-Generate Diffusion Model for Detail-Preserving Image Denoising [16.43285056788183]
We propose a novel approach called the Reconstruct-and-Generate Diffusion Model (RnG) Our method leverages a reconstructive denoising network to recover the majority of the underlying clean signal. It employs a diffusion algorithm to generate residual high-frequency details, thereby enhancing visual quality.
arXiv Detail & Related papers (2023-09-19T16:01:20Z)
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
We introduce a novel sampling framework called Steerable Conditional Diffusion. This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
arXiv Detail & Related papers (2023-08-28T08:47:06Z)
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution [84.73658185158222]
We propose a diffusion model-based super-resolution method called ACDMSR. Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
arXiv Detail & Related papers (2023-07-03T06:49:04Z)
Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
arXiv Detail & Related papers (2023-06-01T03:08:28Z)
DIRE for Diffusion-Generated Image Detection [128.95822613047298]
We propose a novel representation called DIffusion Reconstruction Error (DIRE) DIRE measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images.
arXiv Detail & Related papers (2023-03-16T13:15:03Z)

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