A Single Simple Patch is All You Need for AI-generated Image Detection

• URL: http://arxiv.org/abs/2402.01123v2
• Date: Sat, 20 Apr 2024 04:38:35 GMT
• Title: A Single Simple Patch is All You Need for AI-generated Image Detection
• Authors: Jiaxuan Chen, Jieteng Yao, Li Niu,
• Abstract summary: We find that generative models tend to focus on generating the patches with rich textures to make the images more realistic. In this paper, we propose to exploit the noise pattern of a single simple patch to identify fake images. Our method can achieve state-of-the-art performance on public benchmarks.
• Score: 19.541645669791023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent development of generative models unleashes the potential of generating hyper-realistic fake images. To prevent the malicious usage of fake images, AI-generated image detection aims to distinguish fake images from real images. However, existing method suffer from severe performance drop when detecting images generated by unseen generators. We find that generative models tend to focus on generating the patches with rich textures to make the images more realistic while neglecting the hidden noise caused by camera capture present in simple patches. In this paper, we propose to exploit the noise pattern of a single simple patch to identify fake images. Furthermore, due to the performance decline when handling low-quality generated images, we introduce an enhancement module and a perception module to remove the interfering information. Extensive experiments demonstrate that our method can achieve state-of-the-art performance on public benchmarks.

Related papers

• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• A Sanity Check for AI-generated Image Detection [49.08585395873425]
  We present a sanity check on whether the task of AI-generated image detection has been solved. To quantify the generalization of existing methods, we evaluate 9 off-the-shelf AI-generated image detectors on Chameleon dataset. We propose AIDE (AI-generated Image DEtector with Hybrid Features), which leverages multiple experts to simultaneously extract visual artifacts and noise patterns.
  arXiv  Detail & Related papers  (2024-06-27T17:59:49Z)
• Detection of Synthetic Face Images: Accuracy, Robustness, Generalization [1.757194730633422]
  We find that a simple model trained on a specific image generator can achieve near-perfect accuracy in separating synthetic and real images. The model turned out to be vulnerable to adversarial attacks and does not generalize to unseen generators.
  arXiv  Detail & Related papers  (2024-06-25T13:34:50Z)
• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
  RIGID is a training-free and model-agnostic method for robust AI-generated image detection. RIGID significantly outperforms existing trainingbased and training-free detectors.
  arXiv  Detail & Related papers  (2024-05-30T14:49:54Z)
• Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models [39.33821502730661]
  We present a training-free method to attribute fake images generated by text-to-image models to their source models. By calculating and ranking the similarity of the test image and the candidate images, we can determine the source of the image.
  arXiv  Detail & Related papers  (2024-03-03T11:55:49Z)
• PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection [39.820699370876916]
  We propose a novel AI-generated image detector capable of identifying fake images created by a wide range of generative models. A novel Smash&Reconstruction preprocessing is proposed to erase the global semantic information and enhance texture patches. Our approach outperforms state-of-the-art baselines by a significant margin.
  arXiv  Detail & Related papers  (2023-11-21T07:12:40Z)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors [24.78672820633581]
  Deep generative models can create remarkably fake images while raising concerns about misinformation and copyright infringement. Deepfake detection technique is developed to distinguish between real and fake images. We propose a novel approach called AntifakePrompt, using Vision-Language Models and prompt tuning techniques.
  arXiv  Detail & Related papers  (2023-10-26T14:23:45Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• What makes fake images detectable? Understanding properties that generalize [55.4211069143719]
  Deep networks can still pick up on subtle artifacts in doctored images. We seek to understand what properties of fake images make them detectable. We show a technique to exaggerate these detectable properties.
  arXiv  Detail & Related papers  (2020-08-24T17:50:28Z)
• FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network [19.246576904646172]
  We propose a light-weight fine-tuning neural network-based architecture called FaketNet. Our approach aims to reuse popular pre-trained models with only a few images for fine-tuning to effectively detect fake images. Our tNet achieves an overall accuracy of 9029% in detecting fake images generated from the GANs-based dataset.
  arXiv  Detail & Related papers  (2020-01-05T16:04:17Z)

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.