mlsanityData to 7 Oct 2026

Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality

arXiv 2307.02347 ↗First posted 5 Jul 2023cs.CV cs.CRcited by 44

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification. Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 70 of its 113 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

70 of 113 references.

Closest ancestors · 12

Same direction, newest first

  1. 2023Generalizable Synthetic Image Detection via Language-guided Contrastive Learning23 May
  2. 2023Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images2 Apr
  3. 2023DIRE for Diffusion-Generated Image Detection16 Mar
  4. 2023Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models1 Mar
  5. 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models26 Oct
  6. 2022Towards the Detection of Diffusion Model Deepfakes26 Oct
  7. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation25 Aug
  8. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
  9. 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
  10. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec
  11. 2021Blended Diffusion for Text-driven Editing of Natural Images29 Nov
  12. 2021Palette: Image-to-Image Diffusion Models10 Nov

Other references · 58

  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. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  7. 2020Denoising Diffusion Implicit Modelscited by 9,655
  8. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  9. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  10. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  11. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  12. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
38 more
  1. 2022Scalable Diffusion Models with Transformerscited by 5,931
  2. 2017Neural Discrete Representation Learningcited by 4,650
  3. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  5. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  7. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  8. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  9. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  10. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  11. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  12. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  13. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  14. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
  15. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
  16. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
  17. 2023Extracting Training Data from Diffusion Modelscited by 723
  18. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  19. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 518
  20. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 483
  21. 2022Detecting Deepfakes with Self-Blended Imagescited by 332
  22. 2021The Intrinsic Dimension of Images and Its Impact on Learningcited by 307
  23. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  24. 2018Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionalitycited by 280
  25. 2019Intrinsic dimension of data representations in deep neural networkscited by 276
  26. 2020What makes fake images detectable? Understanding properties that generalizecited by 272
  27. 2023MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizercited by 260
  28. 2022Re-Imagen: Retrieval-Augmented Text-to-Image Generatorcited by 208
  29. 2021Projected GANs Converge Fastercited by 204
  30. 2022Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Qualitycited by 181
  31. 2018Learning Rich Features for Image Manipulation Detectioncited by 154
  32. 2023Hierarchical Fine-Grained Image Forgery Detection and Localizationcited by 134
  33. 2021Generative Adversarial Transformerscited by 120
  34. 2023CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Imagescited by 84
  35. 2019Unmasking DeepFakes with simple Featurescited by 78
  36. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  37. 2018On the Intrinsic Dimensionality of Image Representationscited by 57
  38. 2019Detecting Photoshopped Faces by Scripting Photoshopcited by 55

50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 22; 2025: 13; 2026: 8.

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  1. 2024DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmarkcited by 85
  2. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  3. 2023AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectorscited by 64
  4. 2024WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detectioncited by 31
  5. 2024Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networkscited by 27
  6. 2024Beyond Deepfake Images: Detecting AI-Generated Videoscited by 22
  7. 2024Bi-LORA: A Vision-Language Approach for Synthetic Image Detectioncited by 20
  8. 2024Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videoscited by 20
  9. 2024Latent Watermark: Inject and Detect Watermarks in Latent Diffusion Spacecited by 19
  10. 2025Learning Human-Perceived Fakeness in AI-Generated Videos via Multimodal LLMscited by 16
  11. 2025Few-Shot Learner Generalizes Across AI-Generated Image Detectioncited by 13
  12. 2024Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attackscited by 12
32 more
  1. 2025Methods and Trends in Detecting AI-Generated Images: A Comprehensive Reviewcited by 12
  2. 2025TCFG: Tangential Damping Classifier-free Guidancecited by 12
  3. 2025Measuring the Intrinsic Dimension of Earth Representationscited by 10
  4. 2024Towards the Detection of AI-Synthesized Human Face Imagescited by 8
  5. 2025Towards Explainable Fake Image Detection with Multi-Modal Large Language Modelscited by 8
  6. 2024Harnessing the Power of Large Vision Language Models for Synthetic Image Detectioncited by 7
  7. 2025Explainable Synthetic Image Detection through Diffusion Timestep Ensemblingcited by 7
  8. 2024Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challengescited by 6
  9. 2024COMPOSE: Comprehensive Portrait Shadow Editingcited by 6
  10. 2024Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learningcited by 5
  11. 2024Artifact Feature Purification for Cross-domain Detection of AI-generated Imagescited by 5
  12. 2025Robust AI-Generated Face Detection with Imbalanced Datacited by 5
  13. 2025DINO-Detect: A Simple yet Effective Framework for Blur-Robust AI-Generated Image Detectioncited by 5
  14. 2024The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methodscited by 4
  15. 2025Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulationscited by 4
  16. 2024Unveiling the Truth: Exploring Human Gaze Patterns in Fake Imagescited by 3
  17. 2024Present and Future Generalization of Synthetic Image Detectorscited by 3
  18. 2025X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Modelscited by 3
  19. 2025Noise-Informed Diffusion-Generated Image Detection with Anomaly Attentioncited by 3
  20. 2024FIDAVL: Fake Image Detection and Attribution using Vision-Language Modelcited by 2
  21. 2026A Difference-in-Difference Approach to Detecting AI-Generated Imagescited by 2
  22. 2024Solutions to Deepfakes: Can Camera Hardware, Cryptography, and Deep Learning Verify Real Images?cited by 1
  23. 2024The Tug-of-War Between Deepfake Generation and Detectioncited by 1
  24. 2024Log-normal Mutations and their Use in Detecting Surreptitious Fake Imagescited by 1
  25. 2026Your AI-Generated Image Detector Can Secretly Achieve SOTA Accuracy, If Calibratedcited by 1
  26. 2025Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalizationcited by 0
  27. 2026A Universal Nearest-Neighbor Estimator for Intrinsic Dimensionalitycited by 0
  28. 2026Deepfake Detection Generalization with Diffusion Noisecited by 0
  29. 2026LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detectioncited by 0
  30. 2026AI-Generated Images: What Humans and Machines See When They Look at the Same Imagecited by 0
  31. 2026CHROMA: Detecting AI-Generated Images through Inter-Channel Color-Space Correlationscited by 0
  32. 2026GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectorscited by 0

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  1. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
  2. 2022On the detection of synthetic images generated by diffusion models
  3. 2023Diffusion Noise Feature: Accurate and Fast Generated Image Detection
  4. 2024A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
  5. 2024DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
  6. 2024Adversarial Examples are Misaligned in Diffusion Model Manifolds
  7. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
  8. 2022Progressive Distillation for Fast Sampling of Diffusion Models