# DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis

arXiv: https://arxiv.org/abs/2403.18471 · HTML: https://mlsanity.com/paper/2403.18471/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models](https://mlsanity.com/paper/2210.06998/) (2022)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [Towards the Detection of Diffusion Model Deepfakes](https://mlsanity.com/paper/2210.14571/) (2022)
- [CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images](https://mlsanity.com/paper/2303.14126/) (2023)
- [Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images](https://mlsanity.com/paper/2304.00500/) (2023)

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

- [Can We Leave Deepfake Data Behind in Training Deepfake Detector?](https://mlsanity.com/paper/2408.17052/) (2024)
- [AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models](https://mlsanity.com/paper/2507.02664/) (2025)
- [DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion](https://mlsanity.com/paper/2410.04372/) (2024)
- [Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection](https://mlsanity.com/paper/2411.11396/) (2024)
- [StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model](https://mlsanity.com/paper/2408.05669/) (2024)
- [Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook](https://mlsanity.com/paper/2411.19537/) (2024)
- [AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection](https://mlsanity.com/paper/2504.20865/) (2025)
- [OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection](https://mlsanity.com/paper/2509.09495/) (2025)
- [HFI: A unified framework for training-free detection and implicit watermarking of latent diffusion model generated images](https://mlsanity.com/paper/2412.20704/) (2024)
- [MAVOS-DD: Multilingual Audio-Video Open-Set Deepfake Detection Benchmark](https://mlsanity.com/paper/2505.11109/) (2025)
- [ED4: Explicit Data-level Debiasing for Deepfake Detection](https://mlsanity.com/paper/2408.06779/) (2024)
- [The Coherence Trap: When MLLM-Crafted Narratives Exploit Manipulated Visual Contexts](https://mlsanity.com/paper/2505.17476/) (2025)
- [Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges](https://mlsanity.com/paper/2406.06965/) (2024)
- [Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline](https://mlsanity.com/paper/2412.19685/) (2024)
- [Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach](https://mlsanity.com/paper/2408.16305/) (2024)
- [Bi-Level Optimization for Self-Supervised AI-Generated Face Detection](https://mlsanity.com/paper/2507.22824/) (2025)
- [Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies](https://mlsanity.com/paper/2501.02207/) (2025)
- [SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models](https://mlsanity.com/paper/2506.00562/) (2025)
- [Generalized Design Choices for Deepfake Detectors](https://mlsanity.com/paper/2511.21507/) (2025)
- [A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World](https://mlsanity.com/paper/2512.04837/) (2025)
- [Towards General Visual-Linguistic Face Forgery Detection(V2)](https://mlsanity.com/paper/2502.20698/) (2025)
- [HAMLET-FFD: Hierarchical Adaptive Multi-modal Learning Embeddings Transformation for Face Forgery Detection](https://mlsanity.com/paper/2507.20913/) (2025)
- [Suppressing Gradient Conflict for Generalizable Deepfake Detection](https://mlsanity.com/paper/2507.21530/) (2025)
- [When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection](https://mlsanity.com/paper/2511.18436/) (2025)
- [R2BD: A Reconstruction-Based Method for Generalizable and Efficient Detection of Fake Images](https://mlsanity.com/paper/2601.08867/) (2026)
- [LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection](https://mlsanity.com/paper/2604.04086/) (2026)
- [Deepfake Detection Generalization with Diffusion Noise](https://mlsanity.com/paper/2604.14570/) (2026)
- [The Deepfakes We Missed: We Built Detectors for a Threat That Didn't Arrive](https://mlsanity.com/paper/2605.12075/) (2026)
- [InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection](https://mlsanity.com/paper/2607.16873/) (2026)
- [REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection](https://mlsanity.com/paper/2609.32734/) (2026)

## Abstract (arXiv)

The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks. Existing face forgery datasets have limitations in generating high-quality facial images and addressing the challenges posed by evolving generative techniques. To combat this, we present DiffusionFace, the first diffusion-based face forgery dataset, covering various forgery categories, including unconditional and Text Guide facial image generation, Img2Img, Inpaint, and Diffusion-based facial exchange algorithms. Our DiffusionFace dataset stands out with its extensive collection of 11 diffusion models and the high-quality of the generated images, providing essential metadata and a real-world internet-sourced forgery facial image dataset for evaluation. Additionally, we provide an in-depth analysis of the data and introduce practical evaluation protocols to rigorously assess discriminative models' effectiveness in detecting counterfeit facial images, aiming to enhance security in facial image authentication processes. The dataset is available for download at urlhttps://github.com/Rapisurazurite/DiffFace.