Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive
  Deep Learning Approach
Faysal Mahmud Yusha Abdullah Minhajul Islam Tahsin Aziz
Deepfake technology is widely used, which has led to serious worries about
the authenticity of digital media, making the need for trustworthy deepfake
face recognition techniques more urgent than ever. This study employs a
resource-effective and transparent cost-sensitive deep learning method to
effectively detect deepfake faces in videos. To create a reliable deepfake
detection system, four pre-trained Convolutional Neural Network (CNN) models:
XceptionNet, InceptionResNetV2, EfficientNetV2S, and EfficientNetV2M were used.
FaceForensics++ and CelebDf-V2 as benchmark datasets were used to assess the
performance of our method. To efficiently process video data, key frame
extraction was used as a feature extraction technique. Our main contribution is
to show the models adaptability and effectiveness in correctly identifying
deepfake faces in videos. Furthermore, a cost-sensitive neural network method
was applied to solve the dataset imbalance issue that arises frequently in
deepfake detection. The XceptionNet model on the CelebDf-V2 dataset gave the
proposed methodology a 98% accuracy, which was the highest possible whereas,
the InceptionResNetV2 model, achieves an accuracy of 94% on the FaceForensics++
dataset. Source Code:
https://github.com/Faysal-MD/Unmasking-Deepfake-Faces-from-Videos-An-Explainable-Cost-Sensitive-Deep-Learning-Approach-IEEE2023