# AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark

arXiv: https://arxiv.org/abs/2406.00783 · HTML: https://mlsanity.com/paper/2406.00783/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 123 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 30 of 71)

- [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)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [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)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues](https://mlsanity.com/paper/2007.09355/) (2020)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Learning Models with Uniform Performance via Distributionally Robust Optimization](https://mlsanity.com/paper/1810.08750/) (2018)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)

## Built on by (most cited first, 28 of 28)

- [LEGION: Learning to Ground and Explain for Synthetic Image Detection](https://mlsanity.com/paper/2503.15264/) (2025)
- [Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning](https://mlsanity.com/paper/2408.17065/) (2024)
- [VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models](https://mlsanity.com/paper/2503.06142/) (2025)
- [Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges](https://mlsanity.com/paper/2406.06965/) (2024)
- [Improving Generalization of Medical Image Registration Foundation Model](https://mlsanity.com/paper/2505.06527/) (2025)
- [Preserving AUC Fairness in Learning with Noisy Protected Groups](https://mlsanity.com/paper/2505.18532/) (2025)
- [Robust AI-Generated Face Detection with Imbalanced Data](https://mlsanity.com/paper/2505.02182/) (2025)
- [LaTo: Landmark-tokenized Diffusion Transformer for Fine-grained Human Face Editing](https://mlsanity.com/paper/2509.25731/) (2025)
- [Robust Fairness Vision-Language Learning for Medical Image Analysis](https://mlsanity.com/paper/2505.03153/) (2025)
- [Rethinking Individual Fairness in Deepfake Detection](https://mlsanity.com/paper/2507.14326/) (2025)
- [Fit for Purpose? Deepfake Detection in the Real World](https://mlsanity.com/paper/2510.16556/) (2025)
- [OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations](https://mlsanity.com/paper/2412.02479/) (2024)
- [Fine-Grained DINO Tuning with Dual Supervision for Face Forgery Detection](https://mlsanity.com/paper/2511.12107/) (2025)
- [From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering](https://mlsanity.com/paper/2603.20193/) (2026)
- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)
- [Revisiting Face Forgery Detection: From Facial Representation to Forgery Detection](https://mlsanity.com/paper/2409.16945/) (2024)
- [LAMM-ViT: AI Face Detection via Layer-Aware Modulation of Region-Guided Attention](https://mlsanity.com/paper/2505.07734/) (2025)
- [Reliable and Reproducible Demographic Inference for Fairness in Face Analysis](https://mlsanity.com/paper/2510.20482/) (2025)
- [SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMs](https://mlsanity.com/paper/2511.12404/) (2025)
- [Are Detectors Fair to Indian IP-AIGC? A Cross-Generator Study](https://mlsanity.com/paper/2512.02850/) (2025)
- [Exploring Specular Reflection Inconsistency for Generalizable Face Forgery Detection](https://mlsanity.com/paper/2602.06452/) (2026)
- [Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification](https://mlsanity.com/paper/2603.02475/) (2026)
- [Robust Multi-Source Covid-19 Detection in CT Images](https://mlsanity.com/paper/2604.03320/) (2026)
- [Non-Colliding Biometric Identities for Digital Entities: Geometry, Capacity, and Million-Scale Virtual Identity Provisioning](https://mlsanity.com/paper/2605.18238/) (2026)
- [The Calibrated Deepfake Trust Score (CDTS): Competence-Coupled Trust Degradation Across Deepfake Detectors](https://mlsanity.com/paper/2606.29484/) (2026)
- [Face-Trace: Open-Set Attribution and Progressive Discovery of Synthetic Face Generators](https://mlsanity.com/paper/2607.07545/) (2026)
- [Detecting CSAM Text-to-Image LoRAs From Weights](https://mlsanity.com/paper/2607.25750/) (2026)
- [FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection](https://mlsanity.com/paper/2608.01661/) (2026)

## Abstract (arXiv)

AI-generated faces have enriched human life, such as entertainment, education, and art. However, they also pose misuse risks. Therefore, detecting AI-generated faces becomes crucial, yet current detectors show biased performance across different demographic groups. Mitigating biases can be done by designing algorithmic fairness methods, which usually require demographically annotated face datasets for model training. However, no existing dataset encompasses both demographic attributes and diverse generative methods simultaneously, which hinders the development of fair detectors for AI-generated faces. In this work, we introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake videos, and faces generated by Generative Adversarial Networks and Diffusion Models. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors and provide valuable insights and findings to promote the future fair design of AI face detectors. Our AI-Face dataset and benchmark code are publicly available at https://github.com/Purdue-M2/AI-Face-FairnessBench