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

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arxiv 2402.01123 v2 pith:KRVHZEIQ submitted 2024-02-02 cs.CV

Jiaxuan Chen , Jieteng Yao , Li Niu This is my paper · ORCID
classification cs.CV
keywords imagesfakeperformancesimpleai-generateddetectiongeneratedgenerating
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios

    cs.CV 2025-09 conditional novelty 7.0 of 10

    AI-generated image detectors lose substantial accuracy on images shared over social media or scanned/re-photographed, while humans improve quickly after seeing two examples.

  2. Structured Local Differential Modeling for AI-Generated Image Detection

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    RippleNet detects AI-generated images by encoding multi-directional, multi-scale local pixel differences and using attention over those differences, achieving state-of-the-art cross-generator accuracy in the reported ...

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  5. SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models

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  6. Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images

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    A zero-shot detector for AI-generated images uses curvature and bias criteria from a diffusion model's score function and outperforms prior zero-shot methods on 20 generative model families.

  7. Retrieval-Driven Training-Free AI-Generated Video Attribution

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    A training-free retrieval pipeline using adaptive color transforms, multi-scale quantized residuals, and temporal aggregation attributes AI-generated videos to one of eight generators with 84.6% Rank-1 and 78.3% mAP o...

  8. Perceptual Classifiers: Detecting Generative Images using Perceptual Features

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    A two-layer classifier trained on CONTRIQUE image-quality features achieves state-of-the-art accuracy on GenImage and DRCT-2M fake-image detection benchmarks.

  9. RAID: Towards Robust AI-Generated Image Detection with Bit-Reversed Images

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    Reversing bit-plane weights ('bit-reversed image') plus a gradient-selected 32×32 patch lets a small ResNet detect AI-generated images with state-of-the-art accuracy on many benchmarks.

  10. Few-Shot Learner Generalizes Across AI-Generated Image Detection

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    A Prototypical Network that treats each image generator as a class improves AI-generated image detection on GenImage with 10 samples per unseen generator.

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