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Multi Stage Learning for Deepfake Detection

Introduction

Weights

link

Setup

• Prepare dataset: Split data in train, validation, and testing. These fake and real folders contain fake and real images, respectively.

The directory structure is as follows:

   Data --
         |
         |---train--
         |         |---real --|
         |         |          |-- image1.png
         |         |          |-- image2.png
         |         |                :
         |         |                :
         |         |          |-- imagen.png
         |         |
         |         |---fake --|
         |                    |-- image1.png
         |                    |-- image2.png
         |                          :
         |                          :
         |                    |-- imagem.png
         |
         |
         |---valid
         |
         |---test

• Generate cropped faces dataset: Now, we have to generate cropped faces dataset which can be genrated by following command $ python3 process_data.py --input_folder Data --output_folder cropped_data

• Generate Masked Eye Dataset: $ python3 masking.py --input_folder cropped_data --output_folder Masked_data $ cp -r cropped_data/validation Masked_data $ cp -r cropped_data/test Masked_data

• Run the training script:

python train.py
    -d <training-data-path>
    -m <model-variant>
    -e <num-epochs>
    -p <pretrained-model-file>
    -b <batch-size>
    -t

<training-data-path>: Path to the training data.<br/> <model-variant>: Specify the model variant (ed for Autoencoder or vae for Variational Autoencoder).<br/> <num-epochs>: Number of epochs for training.<br/> <pretrained-model-file> (optional): Specify the filename of a pre-trained model to continue training.<br/> -b (optional): Batch size for training. The default is 32.<br/> -t (optional): Run the test on the test dataset after training.

The model weights and metrics are saved in the weight folder.

• Multi-stage training:

python --d Masked_data --m ed --e 20 -b 32 -t y

python --d simple_data --m ed --e 15 -b 32 -t y -p weight/best_model_ed.pth

python --d Masked_data --m ed --e 15 -b 32 -t y -p weight/best_model_ed.pth

Testing and Heatmaps :

python test.py --dir Processed_Data --model ed --weight weight/best_model_ed.pth --batch_size 32