mlsanityData to 7 Oct 2026

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

arXiv 2402.01123 ↗First posted 2 Feb 2024cs.CVcited by 43

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Abstract (arXiv)

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.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Builds on 39 of its 50 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

39 of 50 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  9. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  10. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  11. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  12. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
27 more
  1. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  2. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  3. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  4. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  5. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
  6. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
  7. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  8. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 518
  9. 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 501
  10. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 483
  11. 2023DIRE for Diffusion-Generated Image Detectioncited by 386
  12. 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Imagecited by 318
  13. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  14. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 278
  15. 2020What makes fake images detectable? Understanding properties that generalizecited by 272
  16. 2019Large Scale Adversarial Representation Learningcited by 238
  17. 2020Global Texture Enhancement for Fake Face Detection in the Wildcited by 220
  18. 2019Detecting GAN generated Fake Images using Co-occurrence Matricescited by 124
  19. 2019STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editingcited by 119
  20. 2023PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detectioncited by 119
  21. 2022Towards the Detection of Diffusion Model Deepfakescited by 111
  22. 2018ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detectioncited by 104
  23. 2022FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbationscited by 75
  24. 2023Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for nowcited by 61
  25. 2021BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detectioncited by 60
  26. 2023GenDet: Towards Good Generalizations for AI-Generated Image Detectioncited by 36
  27. 2019FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Facescited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 11; 2025: 17; 2026: 15.

43 papers.

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  1. 2024A Sanity Check for AI-generated Image Detectioncited by 187
  2. 2025LEGION: Learning to Ground and Explain for Synthetic Image Detectioncited by 65
  3. 2025Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarioscited by 24
  4. 2025All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learningcited by 23
  5. 2024Beyond Deepfake Images: Detecting AI-Generated Videoscited by 22
  6. 2024Revisiting Tampered Scene Text Detection in the Era of Generative AIcited by 21
  7. 2025Few-Shot Learner Generalizes Across AI-Generated Image Detectioncited by 13
  8. 2025Methods and Trends in Detecting AI-Generated Images: A Comprehensive Reviewcited by 12
  9. 2024MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detectioncited by 11
  10. 2025Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Imagescited by 10
  11. 2025LOTA: Bit-Planes Guided AI-Generated Image Detectioncited by 10
  12. 2024TextureCrop: Enhancing Synthetic Image Detection through Texture-based Croppingcited by 9
31 more
  1. 2024Detecting Origin Attribution for Text-to-Image Diffusion Modelscited by 7
  2. 2025Explainable Synthetic Image Detection through Diffusion Timestep Ensemblingcited by 7
  3. 2024Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challengescited by 6
  4. 2024Improving Interpretability and Robustness for the Detection of AI-Generated Imagescited by 5
  5. 2024The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methodscited by 4
  6. 2025Fully AI-Generated Image Detection: Definition, Recent Advances and Challengescited by 4
  7. 2024Detecting the Undetectable: Combining Kolmogorov-Arnold Networks and MLP for AI-Generated Image Detectioncited by 3
  8. 2025SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Modelscited by 3
  9. 2026Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detectioncited by 3
  10. 2026Attribution as Retrieval: Model-Agnostic AI-Generated Image Attributioncited by 3
  11. 2026FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detectioncited by 3
  12. 2024The Visual Counter Turing Test (VCT2): A Benchmark for Evaluating AI-Generated Image Detection and the Visual AI Index (VAI)cited by 1
  13. 2025Provenance Detection for AI-Generated Images: Combining Perceptual Hashing, Homomorphic Encryption, and AI Detection Modelscited by 1
  14. 2025MiraGe: Multimodal Discriminative Representation Learning for Generalizable AI-Generated Image Detectioncited by 1
  15. 2026Your AI-Generated Image Detector Can Secretly Achieve SOTA Accuracy, If Calibratedcited by 1
  16. 2026ImageAttributionBench: How Far Are We from Generalizable Attribution?cited by 1
  17. 2026Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detectioncited by 1
  18. 2025VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systemscited by 0
  19. 2025Perceptual Classifiers: Detecting Generative Images using Perceptual Featurescited by 0
  20. 2025The Unwinnable Arms Race of AI Image Detectioncited by 0
  21. 2025Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetectcited by 0
  22. 2025FUSE: Unifying Spectral and Semantic Cues for Robust AI-Generated Image Detectioncited by 0
  23. 2026Detecting Diffusion-generated Images via Dynamic Assembly Forestscited by 0
  24. 2026Findings of the Counter Turing Test: AI-Generated Image Detectioncited by 0
  25. 2026Chroma Clues: Leveraging Color Statistics to Detect Synthetic Imagescited by 0
  26. 2026Retrieval-Driven Training-Free AI-Generated Video Attributioncited by 0
  27. 2026RAID: Towards Robust AI-Generated Image Detection with Bit-Reversed Imagescited by 0
  28. 2026Scalable Black-Box Model Attribution for Imagescited by 0
  29. 2026Trustworthy synthetic visual media: Evidence across the media lifecyclecited by 0
  30. 2026Look Closer: Patch-wise Supervision for AI-Generated Image Detectioncited by 0
  31. 2026EvoKnow: Continual Knowledge Evolution for AI-Generated Image Detectioncited by 0