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

arXiv: https://arxiv.org/abs/2303.00608 · HTML: https://mlsanity.com/paper/2303.00608/

## Key facts

- Cited by 21 papers in the map (data through 14 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.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 25 of 25)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Diverse Image Synthesis from Semantic Layouts via Conditional IMLE](https://mlsanity.com/paper/1811.12373/) (2018)
- [Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation](https://mlsanity.com/paper/1812.09912/) (2018)
- [Fighting deepfakes by detecting GAN DCT anomalies](https://mlsanity.com/paper/2101.09781/) (2021)
- [Fighting Deepfake by Exposing the Convolutional Traces on Images](https://mlsanity.com/paper/2008.04095/) (2020)
- [A Survey of Machine Learning Techniques in Adversarial Image Forensics](https://mlsanity.com/paper/2010.09680/) (2020)
- [On the Exploitation of Deepfake Model Recognition](https://mlsanity.com/paper/2204.04513/) (2022)
- [Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images](https://mlsanity.com/paper/2203.09928/) (2022)

## Built on by (most cited first, 21 of 21)

- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors](https://mlsanity.com/paper/2310.17419/) (2023)
- [On the Design Fundamentals of Diffusion Models: A Survey](https://mlsanity.com/paper/2306.04542/) (2023)
- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [Generalizable Synthetic Image Detection via Language-guided Contrastive Learning](https://mlsanity.com/paper/2305.13800/) (2023)
- [Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality](https://mlsanity.com/paper/2307.02347/) (2023)
- [Bi-LORA: A Vision-Language Approach for Synthetic Image Detection](https://mlsanity.com/paper/2404.01959/) (2024)
- [NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging](https://mlsanity.com/paper/2310.04429/) (2023)
- [VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models](https://mlsanity.com/paper/2503.06142/) (2025)
- [GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions](https://mlsanity.com/paper/2312.16220/) (2023)
- [Integrating Audio-Visual Features for Multimodal Deepfake Detection](https://mlsanity.com/paper/2310.03827/) (2023)
- [Diffusion Deepfake](https://mlsanity.com/paper/2404.01579/) (2024)
- [Detecting Origin Attribution for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2403.19653/) (2024)
- [Deepfake Media Forensics: State of the Art and Challenges Ahead](https://mlsanity.com/paper/2408.00388/) (2024)
- [FIDAVL: Fake Image Detection and Attribution using Vision-Language Model](https://mlsanity.com/paper/2409.03109/) (2024)
- [Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis](https://mlsanity.com/paper/2310.11204/) (2023)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [CLOFAI: A Dataset of Real And Fake Image Classification Tasks for Continual Learning](https://mlsanity.com/paper/2501.11140/) (2025)
- [LOCKEY: A Novel Approach to Model Authentication and Deepfake Tracking](https://mlsanity.com/paper/2409.07743/) (2024)
- [Multi-bit Audio Watermarking](https://mlsanity.com/paper/2510.01968/) (2025)
- [AI-Generated Images: What Humans and Machines See When They Look at the Same Image](https://mlsanity.com/paper/2605.06143/) (2026)

## 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.