Boosting Deepfake Detection Accuracy with Unsharp Masking and EfficientNet Models

The rapid progress of deepfake technology, fueled by generative adversarial networks (GANs), has increased the challenge of verifying the authenticity of digital media. This study suggests a more powerful deepfake detection framework based on the EfficientNet convolutional neural network family, coupled with an unsharp masking preprocessing method to highlight manipulation artifacts. Based on a big, diverse dataset of over 5000 video samples, the model was trained and tested on several variants of EfficientNets (B0–B4). The results indicate that the integration of unsharp masking significantly improves the model's ability to detect minor irregularities in facial regions, with its best validation accuracy at 97.77% with EfficientNetB4. The method strikes a balance between computational cost and detection accuracy, rendering it applicable to real-world use cases, such as forensic examination and digital content authentication. The stability of the framework across different datasets and manipulation methods highlights its value as a scalable solution for curbing disinformation and protecting media integrity.

@article{Khaled2025,
  title     = {Boosting Deepfake Detection Accuracy with Unsharp Masking and EfficientNet Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Radwa Khaled and Hossam M. Moftah and Fahad Kamal Alsheref and Adel Saad Assiri and Kamel Hussein Rahouma and Mohammed Kayed},
  doi       = {10.14569/IJACSA.2025.0160832},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160832}
}