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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

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.