DIRE for Diffusion-Generated Image Detection

• URL: http://arxiv.org/abs/2303.09295v1
• Date: Thu, 16 Mar 2023 13:15:03 GMT
• Title: DIRE for Diffusion-Generated Image Detection
• Authors: Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, Houqiang Li
• Abstract summary: 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.
• Score: 128.95822613047298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images. We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data. To address this issue, we propose a novel image representation called DIffusion Reconstruction Error (DIRE), which measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. We observe that diffusion-generated images can be approximately reconstructed by a diffusion model while real images cannot. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images. DIRE provides an effective way to detect images generated by most diffusion models, and it is general for detecting generated images from unseen diffusion models and robust to various perturbations. Furthermore, we establish a comprehensive diffusion-generated benchmark including images generated by eight diffusion models to evaluate the performance of diffusion-generated image detectors. Extensive experiments on our collected benchmark demonstrate that DIRE exhibits superiority over previous generated-image detectors. The code and dataset are available at https://github.com/ZhendongWang6/DIRE.

Related papers

• DiffDoctor: Diagnosing Image Diffusion Models Before Treating [57.82359018425674]
  We propose DiffDoctor, a two-stage pipeline to assist image diffusion models in generating fewer artifacts. We collect a dataset of over 1M flawed synthesized images and set up an efficient human-in-the-loop annotation process. The learned artifact detector is then involved in the second stage to tune the diffusion model through assigning a per-pixel confidence map for each image.
  arXiv  Detail & Related papers  (2025-01-21T18:56:41Z)
• FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error [12.888629653445012]
  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.
  arXiv  Detail & Related papers  (2024-12-10T03:02:34Z)
• Exposing the Fake: Effective Diffusion-Generated Images Detection [14.646957596560076]
  This paper proposes a novel detection method called Stepwise Error for Diffusion-generated Image Detection (SeDID) SeDID exploits the unique attributes of diffusion models, namely deterministic reverse and deterministic denoising errors. Our work makes a pivotal contribution to distinguishing diffusion model-generated images, marking a significant step in the domain of artificial intelligence security.
  arXiv  Detail & Related papers  (2023-07-12T16:16:37Z)
• DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models [79.71665540122498]
  We propose a method for detecting unauthorized data usage by planting the injected content into the protected dataset. Specifically, we modify the protected images by adding unique contents on these images using stealthy image warping functions. By analyzing whether the model has memorized the injected content, we can detect models that had illegally utilized the unauthorized data.
  arXiv  Detail & Related papers  (2023-07-06T16:27:39Z)
• Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality [8.968599131722023]
  Diffusion models have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. We propose using the lightweight multi Local Intrinsic Dimensionality (multiLID) for the automatic detection of synthetic images.
  arXiv  Detail & Related papers  (2023-07-05T15:03:10Z)
• Your Diffusion Model is Secretly a Zero-Shot Classifier [90.40799216880342]
  We show that density estimates from large-scale text-to-image diffusion models can be leveraged to perform zero-shot classification. Our generative approach to classification attains strong results on a variety of benchmarks. Our results are a step toward using generative over discriminative models for downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T17:59:56Z)
• SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
  We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
  arXiv  Detail & Related papers  (2022-11-22T18:00:03Z)
• Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance [95.12230117950232]
  We show that a common latent space emerges from two diffusion models trained independently on related domains. Applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors.
  arXiv  Detail & Related papers  (2022-10-11T15:53:52Z)

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.