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deepfake-detection

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This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the documentaion.

  • Updated Jul 28, 2024
  • Jupyter Notebook

Liveness detection SDK Linux - iBeta level 2 compliant 3D passive liveness detection engine which can detect printed photos, video replay, 3D masks, and deepfake threats

  • Updated Oct 24, 2024
  • Python

Face Liveness Detection SDK - iBeta level 2 compliant passive face liveness detection SDK which can detect printed photos, video replay, 3D masks, and deepfake threats

  • Updated Oct 24, 2024

Liveness detection SDK Android - iBeta level 2 compliant 3D passive liveness detection engine which can detect printed photos, video replay, 3D masks, and deepfake threats

  • Updated Nov 27, 2024
  • Java

Code for Video Deepfake Detection model from "Combining EfficientNet and Vision Transformers for Video Deepfake Detection" presented at ICIAP 2021.

  • Updated Dec 7, 2022
  • Jupyter Notebook

3D Passive Face Liveness Detection (Anti-Spoofing) & Deepfake detection. A single image is needed to compute liveness score. 99,67% accuracy on our dataset and perfect scores on multiple public datasets (NUAA, CASIA FASD, MSU...).

  • Updated Dec 3, 2024
  • C++

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