abhijithjadhav/Deepfake_detection_using_deep_learning
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.
Star & Fork Trend (19 data points)
Multi-Source Signals
Growth Velocity
abhijithjadhav/Deepfake_detection_using_deep_learning has +1 stars this period . 7-day velocity: 0.1%.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | Deepfake_detection_using_deep_learning | emotion-recognition-neural-networks | nucleotide-transformer | Neural-SLAM |
|---|---|---|---|---|
| Stars | 847 | 847 | 847 | 846 |
| Forks | 241 | 305 | 92 | 152 |
| Weekly Growth | +1 | +0 | +0 | +0 |
| Language | Jupyter Notebook | Python | Jupyter Notebook | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | GPL-3.0 | MIT | NOASSERTION | MIT |
Capability Radar vs emotion-recognition-neural-networks
Last code push 619 days ago.
Fork-to-star ratio: 28.5%. Active community forking and contributing.
Issue data not yet available.
+1 stars this period — 0.12% growth rate.
Licensed under GPL-3.0. Copyleft — check compatibility requirements.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.