DeeperForensics-1.0

Download the dataset.

Preprocess the videos and labels.

Train a deep learning model (e.g., CNN, RNN) on the training set.

Validate the model on the validation set.

Evaluate the model on the test set.

Implement or obtain pre-trained deepfake detection methods.

Evaluate the methods on the DeeperForensics-1.0 test set.

Compare the performance metrics (accuracy, precision, recall).

Analyze the strengths and weaknesses of each method.

Isolate videos with specific perturbations.

Evaluate the detection performance on these subsets.

Quantify the impact of each perturbation type and intensity.

Develop strategies to mitigate the impact of distortions.

Train detection models on DeeperForensics-1.0 with adversarial training techniques.

Develop models that are invariant to various forgery techniques.

Test the models against new, unseen forgery methods.

Continuously update the models to adapt to emerging threats.

Study the DeepFake Variational Auto-Encoder (DF-VAE) method.

Implement and experiment with variations of face swapping techniques.

Evaluate the quality and realism of the generated videos.

Develop new methods for enhancing face swapping quality.