Using Deep Learning to Recognize Fake Faces

In recent times, many fake faces have been created using deep learning and machine learning. Most fake faces made with deep learning are referred to as “deepfake photos.” Our study’s primary goal is to propose a useful framework for recognizing deep-fake photos using deep learning and transformative learning techniques. This paper proposed convolutional neural network (CNN) models based on deep transfer learning method-ologies in which the designed classifier using global average pooling (GAP), dropout, and a dense layer with two neurons that use SoftMax are substituted for the final fully connected layer in the pretrained models. DenseNet201, the suggested framework, produced the best accuracy of 86.85% for both the deepfake and real picture datasets, while MobileNet produced a lower accuracy of 82.78%. The obtained experimental results showed that the proposed method outperformed other state-of-the-art fake picture discriminators in terms of performance. The proposed architecture helps cybersecurity specialists fight deepfake-related cybercrimes.

@article{Atwan2024,
  title     = {Using Deep Learning to Recognize Fake Faces},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jaffar Atwan and Mohammad Wedyan and Dheeb Albashish and Elaf Aljaafrah and Ryan Alturki and Bandar Alshawi},
  doi       = {10.14569/IJACSA.2024.01501113},
  url       = {https://doi.org/10.14569/IJACSA.2024.01501113}
}