# Community Forensics: Using Thousands of Generators to Train Fake Image Detectors

arXiv: https://arxiv.org/abs/2411.04125 · HTML: https://mlsanity.com/paper/2411.04125/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 95 of its 137 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 30 of 95)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [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)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)

## Built on by (most cited first, 30 of 50)

- [Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review](https://mlsanity.com/paper/2502.15176/) (2025)
- [Scaling Up AI-Generated Image Detection with Generator-Aware Prototypes](https://mlsanity.com/paper/2512.12982/) (2025)
- [Towards Explainable Fake Image Detection with Multi-Modal Large Language Models](https://mlsanity.com/paper/2504.14245/) (2025)
- [Rethinking Cross-Generator Image Forgery Detection through DINOv3](https://mlsanity.com/paper/2511.22471/) (2025)
- [Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection](https://mlsanity.com/paper/2602.10042/) (2026)
- [Venus-DeFakerOne: Unified Fake Image Detection & Localization](https://mlsanity.com/paper/2605.14091/) (2026)
- [Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples](https://mlsanity.com/paper/2509.25682/) (2025)
- [EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection](https://mlsanity.com/paper/2603.17343/) (2026)
- [So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection](https://mlsanity.com/paper/2505.18660/) (2025)
- [DINO-Detect: A Simple yet Effective Framework for Blur-Robust AI-Generated Image Detection](https://mlsanity.com/paper/2511.12511/) (2025)
- [Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision](https://mlsanity.com/paper/2602.19715/) (2026)
- [Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2605.24906/) (2026)
- [Fully AI-Generated Image Detection: Definition, Recent Advances and Challenges](https://mlsanity.com/paper/2502.19716/) (2025)
- [Towards Reliable Identification of Diffusion-based Image Manipulations](https://mlsanity.com/paper/2506.05466/) (2025)
- [Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images](https://mlsanity.com/paper/2510.04225/) (2025)
- [Revisiting Image Manipulation Localization under Realistic Manipulation Scenarios](https://mlsanity.com/paper/2509.20006/) (2025)
- [Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection](https://mlsanity.com/paper/2601.19430/) (2026)
- [Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction](https://mlsanity.com/paper/2602.06676/) (2026)
- [AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection](https://mlsanity.com/paper/2603.23115/) (2026)
- [Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection](https://mlsanity.com/paper/2605.01638/) (2026)
- [Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models](https://mlsanity.com/paper/2606.15880/) (2026)
- [Intermediate Representations are Strong AI-Generated Image Detectors](https://mlsanity.com/paper/2605.04358/) (2026)
- [Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection](https://mlsanity.com/paper/2607.27113/) (2026)
- [ChatGPT Images 2.5 on Forgery Tasks: Testing Advertised Improvements Against Known Answers](https://mlsanity.com/paper/2609.13617/) (2026)
- [Identifying Prompted Artist Names from Generated Images](https://mlsanity.com/paper/2507.18633/) (2025)
- [Supervised Contrastive Learning for Few-Shot AI-Generated Image Detection and Attribution](https://mlsanity.com/paper/2511.16541/) (2025)
- [NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization](https://mlsanity.com/paper/2512.23374/) (2025)
- [Synthetic Image Detection with CLIP: Understanding and Assessing Predictive Cues](https://mlsanity.com/paper/2602.12381/) (2026)
- [Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection](https://mlsanity.com/paper/2606.10309/) (2026)
- [AI-generated Images Challenge Visual Trust in High-risk Scenarios](https://mlsanity.com/paper/2607.22745/) (2026)

## Abstract (arXiv)

One of the key challenges of detecting AI-generated images is spotting images that have been created by previously unseen generative models. We argue that the limited diversity of the training data is a major obstacle to addressing this problem, and we propose a new dataset that is significantly larger and more diverse than prior work. As part of creating this dataset, we systematically download thousands of text-to-image latent diffusion models and sample images from them. We also collect images from dozens of popular open source and commercial models. The resulting dataset contains 2.7M images that have been sampled from 4803 different models. These images collectively capture a wide range of scene content, generator architectures, and image processing settings. Using this dataset, we study the generalization abilities of fake image detectors. Our experiments suggest that detection performance improves as the number of models in the training set increases, even when these models have similar architectures. We also find that detection performance improves as the diversity of the models increases, and that our trained detectors generalize better than those trained on other datasets. The dataset can be found in https://jespark.net/projects/2024/community_forensics