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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
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 7 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. Test-Time Curriculum for Open-Set AIGC Detection

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A curriculum-based test-time adaptation method, using balanced confident pseudo-labels and multi-scale refinement, improves AIGC detector accuracy on unseen generators by 11 to 29 points over its starting detector.

  3. OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction

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    Ontology-aware contrastive pretraining plus note-to-vitals distillation improves MIMIC ICU risk and length-of-stay prediction using only vital signs at inference.

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

    cs.CV 2025-05 reject novelty 6.0 of 10

    SEED is a 91,526-image benchmark of diffusion-generated sequential facial edits with sequence, mask, and prompt annotations, and FAITH adds DWT high-frequency cues to a transformer for edit-sequence detection.

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

    cs.CV 2026-07 conditional novelty 5.0 of 10

    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...

  6. Perceptual Classifiers: Detecting Generative Images using Perceptual Features

    cs.CV 2025-07 conditional novelty 5.0 of 10

    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.

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

    cs.CV 2026-07 conditional novelty 4.0 of 10

    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.

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