Deepfake Detection and Digital Identity Protection: A Machine Learning Approach

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@article{1707998,
      author = {Rohith Vodapally},
      title = {Deepfake Detection and Digital Identity Protection: A Machine Learning Approach},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {3},
      pages = {901-913},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707998.pdf},
      abstract = {The fast development of deepfake technology has established significant challenges for identifying sources in online media while threatening digital security and nutrition about keyboard protection systems. GANs and similar machine learning algorithms empower sophisticated computer software to generate deepfakes that presently fulfil several destructive functions, including identity theft, misinformation spread, and social manipulation attacks. False information presents dangerous hazards for trust relationships when it achieves high-resolution visual quality yet remains challenging to identify. This study investigates the role of machine learning in detecting deepfakes and its integration into a broader framework for digital identity protection. During dataset testing, the research evaluation assesses various machine learning approaches by studying Convolutional Neural Networks, Long Short-Term Memory Networks, and transformer-based systems through FaceForensics++ and Deepfake Detection Challenge (DFDC). The detection accuracy and generalizability success rate achieve higher levels when attention-based techniques operate in combination with ensemble models to analyze diverse manipulation techniques. This research develops an identity protection platform and deepfake detector capabilities by integrating real-time processing mechanisms with digital tracking blo, blockchain technologies and biometric authorization. Integration of this proposed structure brings enhanced detection abilities while developing modern electronic authentication procedures. The proposed work contributes to protecting digital identity through technical deepfake solutions and security and ethics approaches to secure dependable digital communications in today's AI-powered digital environment.},
      keywords = {Deepfake Detection, Digital Identity Protection, Machine Learning, Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs).},
      month = {September},
  }