# DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion

arXiv: https://arxiv.org/abs/2410.04372 · HTML: https://mlsanity.com/paper/2410.04372/

## Key facts

- Cited by 29 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 42 of its 56 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## 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, 30 of 42)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (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)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [UCF: Uncovering Common Features for Generalizable Deepfake Detection](https://mlsanity.com/paper/2304.13949/) (2023)
- [Learning Self-Consistency for Deepfake Detection](https://mlsanity.com/paper/2012.09311/) (2020)
- [Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection](https://mlsanity.com/paper/2203.12208/) (2022)
- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [The DeepFake Detection Challenge (DFDC) Dataset](https://mlsanity.com/paper/2006.07397/) (2020)
- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization](https://mlsanity.com/paper/2210.14457/) (2022)
- [Local Relation Learning for Face Forgery Detection](https://mlsanity.com/paper/2105.02577/) (2021)
- [Dual Contrastive Learning for General Face Forgery Detection](https://mlsanity.com/paper/2112.13522/) (2021)
- [TALL: Thumbnail Layout for Deepfake Video Detection](https://mlsanity.com/paper/2307.07494/) (2023)
- [Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2304.02556/) (2023)
- [Protecting Celebrities from DeepFake with Identity Consistency Transformer](https://mlsanity.com/paper/2203.01318/) (2022)

## Built on by (most cited first, 29 of 29)

- [Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector](https://mlsanity.com/paper/2503.20188/) (2025)
- [Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Content](https://mlsanity.com/paper/2412.12278/) (2024)
- [Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection](https://mlsanity.com/paper/2501.15253/) (2025)
- [Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection](https://mlsanity.com/paper/2602.10042/) (2026)
- [Motion-Aware Caching for Efficient Autoregressive Video Generation](https://mlsanity.com/paper/2605.01725/) (2026)
- [SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detection](https://mlsanity.com/paper/2509.22070/) (2025)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios](https://mlsanity.com/paper/2506.23292/) (2025)
- [Rethinking Individual Fairness in Deepfake Detection](https://mlsanity.com/paper/2507.14326/) (2025)
- [FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection](https://mlsanity.com/paper/2509.16602/) (2025)
- [MS-GAGA: Metric-Selective Guided Adversarial Generation Attack](https://mlsanity.com/paper/2510.12468/) (2025)
- [A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations](https://mlsanity.com/paper/2506.03425/) (2025)
- [SocialDF: Benchmark Dataset and Detection Model for Mitigating Harmful Deepfake Content on Social Media Platforms](https://mlsanity.com/paper/2506.05538/) (2025)
- [A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World](https://mlsanity.com/paper/2512.04837/) (2025)
- [What if AI systems weren't chatbots?](https://mlsanity.com/paper/2605.07896/) (2026)
- [ForensicConcept: Transferable Forensic Concepts for AIGI Detection](https://mlsanity.com/paper/2606.07034/) (2026)
- [Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection](https://mlsanity.com/paper/2411.19715/) (2024)
- [EvolveReason: Self-Evolving Reasoning Paradigm for Explainable Deepfake Facial Image Identification](https://mlsanity.com/paper/2603.07515/) (2026)
- [When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2603.09242/) (2026)
- [Towards General Visual-Linguistic Face Forgery Detection(V2)](https://mlsanity.com/paper/2502.20698/) (2025)
- [Prefill-Guided Thinking for zero-shot detection of AI-generated images](https://mlsanity.com/paper/2506.11031/) (2025)
- [DiffusionFF: A Diffusion-based Framework for Joint Face Forgery Detection and Fine-Grained Artifact Localization](https://mlsanity.com/paper/2508.01873/) (2025)
- [Towards Generalizable Deepfake Detection via Real Distribution Bias Correction](https://mlsanity.com/paper/2603.14005/) (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)
- [ALGOGEN: Tool-Generated Verifiable Traces for Reliable Algorithm Visualization](https://mlsanity.com/paper/2605.12159/) (2026)
- [Toward Calibrated, Fair, and accurate Deepfake Detection](https://mlsanity.com/paper/2606.09881/) (2026)
- [Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)](https://mlsanity.com/paper/2607.18263/) (2026)
- [DF-CBM: Region-Aware Concept Bottleneck Models for Deepfake Detection](https://mlsanity.com/paper/2609.35096/) (2026)

## Abstract (arXiv)

The rapid progress of Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often struggle to generalize to unseen domains due to the diverse nature of facial manipulations. In this paper, we revisit the generation process and identify a universal principle: Deepfake images inherently contain information from both source and target identities, while genuine faces maintain a consistent identity. Building upon this insight, we introduce DiffusionFake, a novel plug-and-play framework that reverses the generative process of face forgeries to enhance the generalization of detection models. DiffusionFake achieves this by injecting the features extracted by the detection model into a frozen pre-trained Stable Diffusion model, compelling it to reconstruct the corresponding target and source images. This guided reconstruction process constrains the detection network to capture the source and target related features to facilitate the reconstruction, thereby learning rich and disentangled representations that are more resilient to unseen forgeries. Extensive experiments demonstrate that DiffusionFake significantly improves cross-domain generalization of various detector architectures without introducing additional parameters during inference. Our Codes are available in https://github.com/skJack/DiffusionFake.git.