mlsanityData to 7 Oct 2026

Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models

arXiv 2303.00608 ↗First posted 1 Mar 2023cs.CVcited by 20

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

Abstract (arXiv)

The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a binary classification task. In this work, the deepfake detection and recognition task was investigated by collecting a dedicated dataset of pristine images and fake ones generated by 9 different Generative Adversarial Network (GAN) architectures and by 4 additional Diffusion Models (DM). A hierarchical multi-level approach was then introduced to solve three different deepfake detection and recognition tasks: (i) Real Vs AI generated; (ii) GANs Vs DMs; (iii) AI specific architecture recognition. Experimental results demonstrated, in each case, more than 97% classification accuracy, outperforming state-of-the-art methods.

Key facts. Cited by 20 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 25 of its 29 references that are in the map. First posted on arXiv 1 Mar 2023.

Builds on

25 of 29 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022On the detection of synthetic images generated by diffusion models1 Nov
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
  3. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec

Other references · 22

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  7. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  8. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  9. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  10. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
  11. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  12. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
10 more
  1. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  2. 2017AttGAN: Facial Attribute Editing by Only Changing What You Wantcited by 241
  3. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  4. 2018Diverse Image Synthesis from Semantic Layouts via Conditional IMLEcited by 60
  5. 2018Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformationcited by 49
  6. 2021Fighting deepfakes by detecting GAN DCT anomaliescited by 39
  7. 2020Fighting Deepfake by Exposing the Convolutional Traces on Imagescited by 24
  8. 2020A Survey of Machine Learning Techniques in Adversarial Image Forensicscited by 14
  9. 2022On the Exploitation of Deepfake Model Recognitioncited by 14
  10. 2022Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Imagescited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 7; 2025: 3; 2026: 1.

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  1. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  2. 2023AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectorscited by 64
  3. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  4. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
  5. 2023Generalizable Synthetic Image Detection via Language-guided Contrastive Learningcited by 48
  6. 2023Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionalitycited by 44
  7. 2024Bi-LORA: A Vision-Language Approach for Synthetic Image Detectioncited by 20
  8. 2023NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imagingcited by 18
  9. 2025VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Modelscited by 12
  10. 2023GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusionscited by 10
  11. 2023Integrating Audio-Visual Features for Multimodal Deepfake Detectioncited by 9
  12. 2024Diffusion Deepfakecited by 8
8 more
  1. 2024Detecting Origin Attribution for Text-to-Image Diffusion Modelscited by 7
  2. 2024Deepfake Media Forensics: State of the Art and Challenges Aheadcited by 6
  3. 2024FIDAVL: Fake Image Detection and Attribution using Vision-Language Modelcited by 2
  4. 2023Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysiscited by 1
  5. 2024MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networkscited by 1
  6. 2025CLOFAI: A Dataset of Real And Fake Image Classification Tasks for Continual Learningcited by 1
  7. 2025Multi-bit Audio Watermarkingcited by 0
  8. 2026AI-Generated Images: What Humans and Machines See When They Look at the Same Imagecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
  2. 2022Towards the Detection of Diffusion Model Deepfakes
  3. 2026Deepfakes and Synthetic Media: Generation, Detection, and Governance
  4. 2025HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection
  5. 2023Undercover Deepfakes: Detecting Fake Segments in Videos
  6. 2025Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review
  7. 2025Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?
  8. 2024Deepfake Generation and Detection: A Benchmark and Survey