mlsanityData to 14 Oct 2026

Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles

arXiv 2310.16388 ↗First posted 25 Oct 2023cs.CVcited by 0

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Abstract (arXiv)

In the dynamic realm of deepfake detection, this work presents an innovative approach to validate video content. The methodology blends advanced 2-dimensional and 3-dimensional Convolutional Neural Networks. The 3D model is uniquely tailored to capture spatiotemporal features via sliding filters, extending through both spatial and temporal dimensions. This configuration enables nuanced pattern recognition in pixel arrangement and temporal evolution across frames. Simultaneously, the 2D model leverages EfficientNet architecture, harnessing auto-scaling in Convolutional Neural Networks. Notably, this ensemble integrates Voting Ensembles and Adaptive Weighted Ensembling. Strategic prioritization of the 3-dimensional model's output capitalizes on its exceptional spatio-temporal feature extraction. Experimental validation underscores the effectiveness of this strategy, showcasing its potential in countering deepfake generation's deceptive practices.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 25 Oct 2023.

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24 of 40 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2017Squeeze-and-Excitation Networkscited by 4,789
  4. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  5. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  6. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  7. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,143
  8. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,101
  9. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
  10. 2018Albumentations: fast and flexible image augmentationscited by 560
  11. 2017Residual Attention Network for Image Classificationcited by 386
  12. 2018Exposing DeepFake Videos By Detecting Face Warping Artifactscited by 319
12 more
  1. 2020Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detectioncited by 287
  2. 2019On the Detection of Digital Face Manipulationcited by 260
  3. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 244
  4. 2018Do GANs leave artificial fingerprints?cited by 190
  5. 2019FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signalscited by 145
  6. 2019BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUscited by 74
  7. 2020Video Face Manipulation Detection Through Ensemble of CNNscited by 63
  8. 2020DeepFake Detection Based on the Discrepancy Between the Face and its Contextcited by 56
  9. 2021Learning with Multiclass AUC: Theory and Algorithmscited by 35
  10. 2020A Convolutional LSTM based Residual Network for Deepfake Video Detectioncited by 26
  11. 2022Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Imagescited by 8
  12. 2017An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videoscited by 6

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