Deepfake Detection Using Machine Learning

@article{1707875,
      author = {Neelakantan J, C Lakshith Appaiah, Rohithashwa R, Prof. Pooja A},
      title = {Deepfake Detection Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {515-518},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707875.pdf},
      abstract = {Deepfake detection has become increasingly challenging due to advancements in computational power and deep learning algorithms. The creation of highly realistic AI-generated videos, commonly known as deepfakes, poses significant threats, including political unrest, fake terrorism events, revenge porn, and blackmail. This work introduces a novel deep learning-based approach to effectively distinguish AI-generated fake videos from real ones. The proposed system combines a ResNeXt convolutional neural network to extract frame-level features with a Long Short-Term Memory (LSTM) recurrent neural network for video classification. It identifies manipulations such as face replacements and reenactments in videos. To enhance real-world performance, the model is trained and evaluated on a diverse, balanced dataset that integrates multiple sources, including FaceForensics++, the Deepfake Detection Challenge, and Celeb-DF. This straightforward yet robust method demonstrates competitive results in combating deepfake threats using AI.},
      keywords = {Deepfake Detection, ResNeXt, LSTM, Artificial Intelligence, Video Manipulation, Face Replacement, Reenactment, Convolutional Neural Network, Recurrent Neural Network, FaceForensics++, Celeb-DF, AI-Generated Videos.},
      month = {April},
  }