mlsanityData to 7 Oct 2026

DefakeHop++: An Enhanced Lightweight Deepfake Detector

arXiv 2205.00211 ↗First posted 30 Apr 2022cs.CVcited by 5

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Abstract (arXiv)

On the basis of DefakeHop, an enhanced lightweight Deepfake detector called DefakeHop++ is proposed in this work. The improvements lie in two areas. First, DefakeHop examines three facial regions (i.e., two eyes and mouth) while DefakeHop++ includes eight more landmarks for broader coverage. Second, for discriminant features selection, DefakeHop uses an unsupervised approach while DefakeHop++ adopts a more effective approach with supervision, called the Discriminant Feature Test (DFT). In DefakeHop++, rich spatial and spectral features are first derived from facial regions and landmarks automatically. Then, DFT is used to select a subset of discriminant features for classifier training. As compared with MobileNet v3 (a lightweight CNN model of 1.5M parameters targeting at mobile applications), DefakeHop++ has a model of 238K parameters, which is 16% of MobileNet v3. Furthermore, DefakeHop++ outperforms MobileNet v3 in Deepfake image detection performance in a weakly-supervised setting.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 30 Apr 2022.

Builds on

33 of 43 references.

References · 33

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21 more
  1. 2019Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videoscited by 130
  2. 2018Two-Stream Neural Networks for Tampered Face Detectioncited by 125
  3. 2019Use of a Capsule Network to Detect Fake Images and Videoscited by 63
  4. 2021Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Featurescited by 57
  5. 2018Interpretable Convolutional Neural Networks via Feedforward Designcited by 49
  6. 2019PointHop: An Explainable Machine Learning Method for Point Cloud Classificationcited by 46
  7. 2020PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classificationcited by 37
  8. 2016Understanding Convolutional Neural Networks with A Mathematical Modelcited by 33
  9. 2021DefakeHop: A Light-Weight High-Performance Deepfake Detectorcited by 31
  10. 2021R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Methodcited by 26
  11. 2022On Supervised Feature Selection from High Dimensional Feature Spacescited by 23
  12. 2020PointHop++: A Lightweight Learning Model on Point Sets for 3D Classificationcited by 22
  13. 2021Deepfake Detection Scheme Based on Vision Transformer and Distillationcited by 22
  14. 2021BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosiscited by 20
  15. 2021VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRIcited by 18
  16. 2020Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentationcited by 17
  17. 2020FaceHop: A Light-Weight Low-Resolution Face Gender Classification Methodcited by 16
  18. 2021AnomalyHop: An SSL-based Image Anomaly Localization Methodcited by 12
  19. 2020Low-Resolution Face Recognition In Resource-Constrained Environmentscited by 10
  20. 2021Geo-DefakeHop: High-Performance Geographic Fake Image Detectioncited by 9
  21. 2019PixelHop: A Successive Subspace Learning (SSL) Method for Object Classificationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

5 papers.

  1. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  2. 2022PAGER: Progressive Attribute-Guided Extendable Robust Image Generationcited by 5
  3. 2023Enhancing Edge Intelligence with Highly Discriminant LNT Featurescited by 3
  4. 2025Towards Sustainable Universal Deepfake Detection with Frequency-Domain Maskingcited by 0
  5. 2026Adaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environmentscited by 0