# Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models

arXiv: https://arxiv.org/abs/2309.02218 · HTML: https://mlsanity.com/paper/2309.02218/

## Key facts

- Cited by 36 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 27 of its 36 references that are in the map.
- First posted on arXiv 5 Sep 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, 27 of 27)

- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (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)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection](https://mlsanity.com/paper/2001.03024/) (2020)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [Two-branch Recurrent Network for Isolating Deepfakes in Videos](https://mlsanity.com/paper/2008.03412/) (2020)
- [Exploring Temporal Coherence for More General Video Face Forgery Detection](https://mlsanity.com/paper/2108.06693/) (2021)
- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis](https://mlsanity.com/paper/2103.05630/) (2021)
- [ID-Reveal: Identity-aware DeepFake Video Detection](https://mlsanity.com/paper/2012.02512/) (2020)
- [Protecting Celebrities from DeepFake with Identity Consistency Transformer](https://mlsanity.com/paper/2203.01318/) (2022)
- [Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features](https://mlsanity.com/paper/2104.04480/) (2021)
- [Face Forgery Detection by 3D Decomposition](https://mlsanity.com/paper/2011.09737/) (2020)
- [Adversarial Perturbations Fool Deepfake Detectors](https://mlsanity.com/paper/2003.10596/) (2020)

## Built on by (most cited first, 30 of 36)

- [DF40: Toward Next-Generation Deepfake Detection](https://mlsanity.com/paper/2406.13495/) (2024)
- [SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model](https://mlsanity.com/paper/2412.04292/) (2024)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2402.04178/) (2024)
- [AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark](https://mlsanity.com/paper/2406.00783/) (2024)
- [Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?](https://mlsanity.com/paper/2402.03214/) (2024)
- [Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors](https://mlsanity.com/paper/2411.07472/) (2024)
- [GM-DF: Generalized Multi-Scenario Deepfake Detection](https://mlsanity.com/paper/2406.20078/) (2024)
- [Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective](https://mlsanity.com/paper/2404.19382/) (2024)
- [OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection](https://mlsanity.com/paper/2509.09495/) (2025)
- [Masked Conditional Diffusion Model for Enhancing Deepfake Detection](https://mlsanity.com/paper/2402.00541/) (2024)
- [Diffusion Deepfake](https://mlsanity.com/paper/2404.01579/) (2024)
- [DFBench: Benchmarking Deepfake Image Detection Capability of Large Multimodal Models](https://mlsanity.com/paper/2506.03007/) (2025)
- [Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges](https://mlsanity.com/paper/2406.06965/) (2024)
- [ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation](https://mlsanity.com/paper/2511.14259/) (2025)
- [Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization](https://mlsanity.com/paper/2411.08148/) (2024)
- [DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios](https://mlsanity.com/paper/2506.23292/) (2025)
- [A Large-scale Universal Evaluation Benchmark For Face Forgery Detection](https://mlsanity.com/paper/2406.09181/) (2024)
- [SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models](https://mlsanity.com/paper/2506.00562/) (2025)
- [MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios](https://mlsanity.com/paper/2509.05592/) (2025)
- [LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verification](https://mlsanity.com/paper/2407.18614/) (2024)
- [ExpertGen: Training-Free Expert Guidance for Controllable Text-to-Face Generation](https://mlsanity.com/paper/2505.17256/) (2025)
- [Unraveling Hidden Representations: A Multi-Modal Layer Analysis for Better Synthetic Content Forensics](https://mlsanity.com/paper/2508.00784/) (2025)
- [ERF-BA-TFD+: A Multimodal Model for Audio-Visual Deepfake Detection](https://mlsanity.com/paper/2508.17282/) (2025)
- [NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization](https://mlsanity.com/paper/2512.23374/) (2025)
- [Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations](https://mlsanity.com/paper/2312.08034/) (2023)
- [Attack-Aware Deepfake Detection under Counter-Forensic Manipulations](https://mlsanity.com/paper/2512.22303/) (2025)
- [Deepfake Detection Generalization with Diffusion Noise](https://mlsanity.com/paper/2604.14570/) (2026)
- [DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection](https://mlsanity.com/paper/2604.16987/) (2026)
- [Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance](https://mlsanity.com/paper/2606.19195/) (2026)

## Abstract (arXiv)

The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information. To tackle this, we present a thorough investigation into how deepfakes are produced and how they can be identified. The cornerstone of our research is a rich collection of artificial celebrity faces, titled DeepFakeFace (DFF). We crafted the DFF dataset using advanced diffusion models and have shared it with the community through online platforms. This data serves as a robust foundation to train and test algorithms designed to spot deepfakes. We carried out a thorough review of the DFF dataset and suggest two evaluation methods to gauge the strength and adaptability of deepfake recognition tools. The first method tests whether an algorithm trained on one type of fake images can recognize those produced by other methods. The second evaluates the algorithm's performance with imperfect images, like those that are blurry, of low quality, or compressed. Given varied results across deepfake methods and image changes, our findings stress the need for better deepfake detectors. Our DFF dataset and tests aim to boost the development of more effective tools against deepfakes.