Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets.

@article{Aslam2025,
  title     = {Extracting Facial Features to Detect Deepfake Videos Using Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ayesha Aslam and Jamaluddin Mir and Gohar Zaman and Atta Rahman and Asiya Abdus Salam and Farhan Ali and Jamal Alhiyafi and Aghiad Bakry and Mustafa Jamal Gul and Mohammed Gollapalli and Maqsood Mahmud},
  doi       = {10.14569/IJACSA.2025.0160483},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160483}
}