# DefakeHop: A Light-Weight High-Performance Deepfake Detector

arXiv: https://arxiv.org/abs/2103.06929 · HTML: https://mlsanity.com/paper/2103.06929/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 20 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 16 of 16)

- [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)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [Use of a Capsule Network to Detect Fake Images and Videos](https://mlsanity.com/paper/1910.12467/) (2019)
- [Interpretable Convolutional Neural Networks via Feedforward Design](https://mlsanity.com/paper/1810.02786/) (2018)
- [PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification](https://mlsanity.com/paper/2002.03141/) (2020)
- [Understanding Convolutional Neural Networks with A Mathematical Model](https://mlsanity.com/paper/1609.04112/) (2016)
- [DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance](https://mlsanity.com/paper/2004.07532/) (2020)
- [FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method](https://mlsanity.com/paper/2007.09510/) (2020)
- [Low-Resolution Face Recognition In Resource-Constrained Environments](https://mlsanity.com/paper/2011.11674/) (2020)

## Built on by (most cited first, 30 of 31)

- [Green Learning: Introduction, Examples and Outlook](https://mlsanity.com/paper/2210.00965/) (2022)
- [BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis](https://mlsanity.com/paper/2108.04938/) (2021)
- [Testing Human Ability To Detect Deepfake Images of Human Faces](https://mlsanity.com/paper/2212.05056/) (2022)
- [AnomalyHop: An SSL-based Image Anomaly Localization Method](https://mlsanity.com/paper/2105.03797/) (2021)
- [MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection](https://mlsanity.com/paper/2310.02234/) (2023)
- [SAGI: Semantically Aligned and Uncertainty Guided AI Image Inpainting](https://mlsanity.com/paper/2502.06593/) (2025)
- [E-PixelHop: An Enhanced PixelHop Method for Object Classification](https://mlsanity.com/paper/2107.02966/) (2021)
- [Geo-DefakeHop: High-Performance Geographic Fake Image Detection](https://mlsanity.com/paper/2110.09795/) (2021)
- [SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection](https://mlsanity.com/paper/2210.06105/) (2022)
- [An Overview on Generative AI at Scale with Edge-Cloud Computing](https://mlsanity.com/paper/2306.17170/) (2023)
- [TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation](https://mlsanity.com/paper/2107.04020/) (2021)
- [GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds](https://mlsanity.com/paper/2109.11835/) (2021)
- [A-PixelHop: A Green, Robust and Explainable Fake-Image Detector](https://mlsanity.com/paper/2111.04012/) (2021)
- [Self-Supervised Graph Transformer for Deepfake Detection](https://mlsanity.com/paper/2307.15019/) (2023)
- [CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation](https://mlsanity.com/paper/2505.15233/) (2025)
- [DefakeHop++: An Enhanced Lightweight Deepfake Detector](https://mlsanity.com/paper/2205.00211/) (2022)
- [PAGER: Progressive Attribute-Guided Extendable Robust Image Generation](https://mlsanity.com/paper/2206.00162/) (2022)
- [Bridging Gap between Image Pixels and Semantics via Supervision: A Survey](https://mlsanity.com/paper/2107.13757/) (2021)
- [GENHOP: An Image Generation Method Based on Successive Subspace Learning](https://mlsanity.com/paper/2210.03689/) (2022)
- [Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking](https://mlsanity.com/paper/2206.09061/) (2022)
- [Enhancing Edge Intelligence with Highly Discriminant LNT Features](https://mlsanity.com/paper/2312.14968/) (2023)
- [Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes](https://mlsanity.com/paper/2409.10889/) (2024)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [GreenCOD: A Green Camouflaged Object Detection Method](https://mlsanity.com/paper/2405.16144/) (2024)
- [Green Steganalyzer: A Green Learning Approach to Image Steganalysis](https://mlsanity.com/paper/2306.04008/) (2023)
- [Combating Falsification of Speech Videos with Live Optical Signatures (Extended Version)](https://mlsanity.com/paper/2504.21846/) (2025)
- [De-Fake: Style based Anomaly Deepfake Detection](https://mlsanity.com/paper/2507.03334/) (2025)
- [A Tiny Machine Learning Model for Point Cloud Object Classification](https://mlsanity.com/paper/2303.10898/) (2023)
- [Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review](https://mlsanity.com/paper/2401.06999/) (2024)
- [GWPT: A Green Word-Embedding-based POS Tagger](https://mlsanity.com/paper/2401.07475/) (2024)

## Abstract (arXiv)

A light-weight high-performance Deepfake detection method, called DefakeHop, is proposed in this work. State-of-the-art Deepfake detection methods are built upon deep neural networks. DefakeHop extracts features automatically using the successive subspace learning (SSL) principle from various parts of face images. The features are extracted by c/w Saab transform and further processed by our feature distillation module using spatial dimension reduction and soft classification for each channel to get a more concise description of the face. Extensive experiments are conducted to demonstrate the effectiveness of the proposed DefakeHop method. With a small model size of 42,845 parameters, DefakeHop achieves state-of-the-art performance with the area under the ROC curve (AUC) of 100%, 94.95%, and 90.56% on UADFV, Celeb-DF v1 and Celeb-DF v2 datasets, respectively.