LaRE$^2$: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection

• URL: http://arxiv.org/abs/2403.17465v4
• Date: Fri, 21 Feb 2025 12:51:57 GMT
• Title: LaRE$^2$: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
• Abstract summary: We come up with the Latent Reconstruction Error (LaRE), the first reconstruction-error based feature in the latent space for generated image detection.<n>To exploit LaRE, we propose an Error-Guided feature REfinement module (EGRE), which can refine the image feature guided by LaRE to enhance the discriminativeness of the feature.<n>Our EGRE utilizes an align-then-refine mechanism, which effectively refines the image feature for generated-image detection from both spatial and channel perspectives.
• Score: 31.009153793849183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution of Diffusion Models has dramatically improved image generation quality, making it increasingly difficult to differentiate between real and generated images. This development, while impressive, also raises significant privacy and security concerns. In response to this, we propose a novel Latent REconstruction error guided feature REfinement method (LaRE^2) for detecting the diffusion-generated images. We come up with the Latent Reconstruction Error (LaRE), the first reconstruction-error based feature in the latent space for generated image detection. LaRE surpasses existing methods in terms of feature extraction efficiency while preserving crucial cues required to differentiate between the real and the fake. To exploit LaRE, we propose an Error-Guided feature REfinement module (EGRE), which can refine the image feature guided by LaRE to enhance the discriminativeness of the feature. Our EGRE utilizes an align-then-refine mechanism, which effectively refines the image feature for generated-image detection from both spatial and channel perspectives. Extensive experiments on the large-scale GenImage benchmark demonstrate the superiority of our LaRE^2, which surpasses the best SoTA method by up to 11.9%/12.1% average ACC/AP across 8 different image generators. LaRE also surpasses existing methods in terms of feature extraction cost, delivering an impressive speed enhancement of 8 times. Code is available.

Related papers

• When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution [47.89708788628452]
  We propose a pixel-grounded super-resolution (PGSR) framework that preserves LR-observed pixel evidence before VAE compression and reuses it throughout restoration.<n>Experiments demonstrate that PGSR improves the realism--fidelity trade-off and produces more faithful, visually convincing results than existing latent generative SR approaches.
  arXiv  Detail & Related papers  (2026-08-10T05:24:39Z)
• R$^2$BD: A Reconstruction-Based Method for Generalizable and Efficient Detection of Fake Images [31.9904761238593]
  We propose a novel fake image detection framework, called R$2$BD, built upon two key designs.<n>Experiments on the benchmark from 10 public datasets demonstrate that R$2$BD is over 22$times$ faster than existing reconstruction-based methods.<n>In cross-dataset evaluations, it outperforms state-of-the-art methods by an average of 13.87%.
  arXiv  Detail & Related papers  (2026-01-11T02:31:51Z)
• GRRE: Leveraging G-Channel Removed Reconstruction Error for Robust Detection of AI-Generated Images [21.903079107456424]
  G-channel Removed Reconstruction Error (GRRE) is a simple yet effective method that exploits this discrepancy for robust AI-generated image detection.<n> GRRE consistently achieves high detection accuracy across multiple generative models, including those unseen during training.<n>These results highlight the potential of channel-removal-based reconstruction as a powerful forensic tool for safeguarding image authenticity in the era of generative AI.
  arXiv  Detail & Related papers  (2026-01-06T04:53:10Z)
• Revisiting Reconstruction-based AI-generated Image Detection: A Geometric Perspective [50.83711509908479]
  We introduce the Jacobian-Spectral Lower Bound for reconstruction error from a geometric perspective.<n>We show that real images off the reconstruction manifold exhibit a non-trivial error lower bound, while generated images on the manifold have near-zero error.<n>We propose ReGap, a training-free method that computes dynamic reconstruction error by leveraging structured editing operations.
  arXiv  Detail & Related papers  (2025-10-29T03:45:03Z)
• AEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction [25.525545133210805]
  AEDR (AutoEncoder Double-Reconstruction) is a training-free attribution method designed for generative models with continuous autoencoders.<n>It achieves 25.5% higher attribution accuracy than existing reconstruction-based methods, while requiring only 1% of the computational time.
  arXiv  Detail & Related papers  (2025-07-25T06:34:58Z)
• Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution [7.920423405957888]
  We propose a novel single-step diffusion approach designed to enhance both efficiency and visual quality in RSISR tasks. The proposed LCMSR reduces the iterative steps of traditional diffusion models from 50-1000 or more to just a single step. Experimental results demonstrate that LCMSR effectively balances efficiency and performance, achieving inference times comparable to non-diffusion models.
  arXiv  Detail & Related papers  (2025-03-25T09:56:21Z)
• FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution [48.88184541515326]
  We propose a simple and effective method, named FaithDiff, to fully harness the power of latent diffusion models (LDMs) for faithful image SR. In contrast to existing diffusion-based SR methods that freeze the diffusion model pre-trained on high-quality images, we propose to unleash the diffusion prior to identify useful information and recover faithful structures.
  arXiv  Detail & Related papers  (2024-11-27T23:58:03Z)
• ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution [28.945663118445037]
  Real-world image super-resolution (Real-ISR) aims at restoring high-quality (HQ) images from low-quality (LQ) inputs corrupted by unknown and complex degradations. We introduce ConsisSR to handle both semantic and pixel-level consistency.
  arXiv  Detail & Related papers  (2024-10-17T17:41:52Z)
• Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration [59.744840744491945]
  In this paper, we reformulate the trajectory optimization of this kind of method, focusing on enhancing both reconstruction quality and efficiency.<n>To mitigate the considerable computational burden associated with iterative sampling, we propose cost-aware trajectory distillation.<n>We fine-tune a foundational diffusion model (FLUX) with 12B parameters by using our algorithms, producing a unified framework for handling 7 kinds of image restoration tasks.
  arXiv  Detail & Related papers  (2024-10-07T07:46:08Z)
• Effective Diffusion Transformer Architecture for Image Super-Resolution [63.254644431016345]
  We design an effective diffusion transformer for image super-resolution (DiT-SR) In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks. We analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module.
  arXiv  Detail & Related papers  (2024-09-29T07:14:16Z)
• One-step Generative Diffusion for Realistic Extreme Image Rescaling [47.89362819768323]
  We propose a novel framework called One-Step Image Rescaling Diffusion (OSIRDiff) for extreme image rescaling. OSIRDiff performs rescaling operations in the latent space of a pre-trained autoencoder. It effectively leverages powerful natural image priors learned by a pre-trained text-to-image diffusion model.
  arXiv  Detail & Related papers  (2024-08-17T09:51:42Z)
• AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error [15.46508882889489]
  A key enabler for generating high-resolution images with low computational cost has been the development of latent diffusion models (LDMs) LDMs perform the denoising process in the low-dimensional latent space of a pre-trained autoencoder (AE) instead of the high-dimensional image space. We propose a novel detection method which exploits an inherent component of LDMs: the AE used to transform images between image and latent space.
  arXiv  Detail & Related papers  (2024-01-31T14:36:49Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model [59.08821399652483]
  Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion model (DM)-based methods have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. We propose to leverage DM within a compact latent space to generate concise guidance priors and introduce a novel solution called Reti-Diff for the IDIR task. Reti-Diff comprises two key components: the Retinex-based latent DM (RLDM) and the Retinex-guided transformer (RG
  arXiv  Detail & Related papers  (2023-11-20T09:55:06Z)
• Robust GAN inversion [5.1359892878090845]
  We propose an approach which works in native latent space $W$ and tunes the generator network to restore missing image details. We demonstrate the effectiveness of our approach on two complex datasets: Flickr-Faces-HQ and LSUN Church.
  arXiv  Detail & Related papers  (2023-08-31T07:47:11Z)
• 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)
• AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE [8.294692832460546]
  introspective models like IntroVAE and S-IntroVAE have excelled in image generation and reconstruction tasks. We propose a new variation of IntroVAE called Adversarial Similarity Distance Introspective Variational Autoencoder (AS-IntroVAE)
  arXiv  Detail & Related papers  (2022-06-28T11:40:17Z)
• Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection [63.18846475183332]
  We aim to develop an efficient and compact deep network for RGB-D salient object detection. We propose a progressively guided alternate refinement network to refine it. Our model outperforms existing state-of-the-art approaches by a large margin.
  arXiv  Detail & Related papers  (2020-08-17T02:55:06Z)

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.