Unmasking Deepfake Faces from Videos An Explainable Cost-Sensitive Deep Learning Approach

This repository contains the code and datasets used in the paper titled "Unmasking Deepfake Faces from Videos An Explainable Cost-Sensitive Deep Learning Approach" accepted and presented at the 26th International Conference on Computer and Information Technology (ICCIT) 2023.

Paper Link: PDF

Table of Contents

• Dataset
• Result
• Citation

Dataset

We used publicly available datasets they are CelbDF-V2 and FaceForensics++

Result

Performance Metrics of Weighted Average on CelebDf-V2 Dataset

Model	Accuracy	Precision	Recall	F1 Score
XceptionNet	98%	0.98	0.98	0.98
InceptionResNetV2	0.97	0.97	0.97	0.97
EfficientNetV2S	0.97	0.97	0.97	0.97
EfficientNetV2M	0.97	0.97	0.97	0.97

Performance Metrics of Weighted Average on FaceForensics++ Dataset

Model	Accuracy	Precision	Recall	F1 Score
InceptionResNetV2	94%	0.94	0.94	0.94
XceptionNet	93%	0.93	0.93	0.93
EfficientNetV2S	92%	0.92	0.92	0.92
EfficientNetV2M	88%	0.89	0.88	0.88

Citation

If you found this code helpful please consider citing,

@inproceedings{mahmud2023unmasking,
  title={Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach},
  author={Mahmud, Faysal and Abdullah, Yusha and Islam, Minhajul and Aziz, Tahsin},
  booktitle={2023 26th International Conference on Computer and Information Technology (ICCIT)},
  pages={1--6},
  year={2023},
  organization={IEEE}
}

License

This repository is licensed under the MIT License. See the LICENSE file for more information.