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__pycache__
__pycache__
 
 
Deepfake.py
Deepfake.py
 
 
api.py
api.py
 
 
app.py
app.py
 
 
dataset-LENOVO_IDEAPAD.py
dataset-LENOVO_IDEAPAD.py
 
 
dataset.py
dataset.py
 
 
dataset_torch.py
dataset_torch.py
 
 
dataset_torch_new.py
dataset_torch_new.py
 
 
dataset_torch_new.py.bak
dataset_torch_new.py.bak
 
 
evaluate-LENOVO_IDEAPAD.py
evaluate-LENOVO_IDEAPAD.py
 
 
evaluate.py
evaluate.py
 
 
evaluate_torch.py
evaluate_torch.py
 
 
metrics.py
metrics.py
 
 
model_architecture.py
model_architecture.py
 
 
model_torch.py
model_torch.py
 
 
plot_learning_curves.py
plot_learning_curves.py
 
 
prediction.py
prediction.py
 
 
requirements.txt
requirements.txt
 
 
roc_curve.py
roc_curve.py
 
 
server.py
server.py
 
 
temp.py
temp.py
 
 
train-LENOVO_IDEAPAD.py
train-LENOVO_IDEAPAD.py
 
 
train.py
train.py
 
 
train_torch.py
train_torch.py
 
 
training_log.csv
training_log.csv
 
 

About

A deepfake detection system using CNN, ResNet50, VGG19, and VGG16+LSTM was tested, with the hybrid model performing best (96.79% training accuracy, 95.96% validation accuracy, 0.0854 loss). By combining spatial and temporal features, it outperforms existing models, enhancing digital media authenticity.

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