# FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge

arXiv: https://arxiv.org/abs/2404.13872 · HTML: https://mlsanity.com/paper/2404.13872/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 43 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [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)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions](https://mlsanity.com/paper/2003.01826/) (2020)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [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)
- [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)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis](https://mlsanity.com/paper/2103.05630/) (2021)
- [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)
- [Face Forensics in the Wild](https://mlsanity.com/paper/2103.16076/) (2021)
- [Unmasking DeepFakes with simple Features](https://mlsanity.com/paper/1911.00686/) (2019)
- [Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning](https://mlsanity.com/paper/2112.13977/) (2021)
- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.07698/) (2018)

## Built on by (most cited first, 30 of 48)

- [FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error](https://mlsanity.com/paper/2412.07140/) (2024)
- [Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector](https://mlsanity.com/paper/2503.20188/) (2025)
- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes](https://mlsanity.com/paper/2504.04827/) (2025)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning](https://mlsanity.com/paper/2602.08828/) (2026)
- [Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook](https://mlsanity.com/paper/2411.19537/) (2024)
- [OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection](https://mlsanity.com/paper/2509.09495/) (2025)
- [UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization](https://mlsanity.com/paper/2510.03161/) (2025)
- [Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges](https://mlsanity.com/paper/2406.06965/) (2024)
- [AvatarShield: Visual Reinforcement Learning for Human-Centric Synthetic Video Detection](https://mlsanity.com/paper/2505.15173/) (2025)
- [PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection](https://mlsanity.com/paper/2509.26272/) (2025)
- [Open-World Deepfake Attribution via Confidence-Aware Asymmetric Learning](https://mlsanity.com/paper/2512.12667/) (2025)
- [A Quality-Centric Framework for Generic Deepfake Detection](https://mlsanity.com/paper/2411.05335/) (2024)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting](https://mlsanity.com/paper/2412.01091/) (2024)
- [Towards Generalized Proactive Defense against Face Swapping with Contour-Hybrid Watermark](https://mlsanity.com/paper/2505.19081/) (2025)
- [Rethinking Individual Fairness in Deepfake Detection](https://mlsanity.com/paper/2507.14326/) (2025)
- [WMamba: Wavelet-based Mamba for Face Forgery Detection](https://mlsanity.com/paper/2501.09617/) (2025)
- [VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection](https://mlsanity.com/paper/2604.13660/) (2026)
- [Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection](https://mlsanity.com/paper/2411.19715/) (2024)
- [SELFI: Selective Fusion of Identity for Generalizable Deepfake Detection](https://mlsanity.com/paper/2506.17592/) (2025)
- [Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation](https://mlsanity.com/paper/2511.07051/) (2025)
- [FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videos](https://mlsanity.com/paper/2512.14601/) (2025)
- [AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection](https://mlsanity.com/paper/2604.16207/) (2026)
- [HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection](https://mlsanity.com/paper/2605.26421/) (2026)
- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)
- [Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning](https://mlsanity.com/paper/2607.28769/) (2026)
- [Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization](https://mlsanity.com/paper/2608.20913/) (2026)
- [Prefill-Guided Thinking for zero-shot detection of AI-generated images](https://mlsanity.com/paper/2506.11031/) (2025)

## Abstract (arXiv)

Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in spatial domain. While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting the learning of generic forgery traces in-depth. To address this, this paper introduces em FreqBlender, a new method that can generate pseudo-fake faces by blending frequency knowledge. Concretely, we investigate the major frequency components and propose a Frequency Parsing Network to adaptively partition frequency components related to forgery traces. Then we blend this frequency knowledge from fake faces into real faces to generate pseudo-fake faces. Since there is no ground truth for frequency components, we describe a dedicated training strategy by leveraging the inner correlations among different frequency knowledge to instruct the learning process. Experimental results demonstrate the effectiveness of our method in enhancing DeepFake detection, making it a potential plug-and-play strategy for other methods.