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March 19, 2026Journal of King Saud University - Computer and Information SciencesOpen Access

Integrating enhanced feature fusion with adaptive hidden semi-markov model and deep neural network for robust deepfake detection in Internet of Things-based edge computing environments

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Authors

JAJawhara AljabriUniversity of Tabuk

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Implication

Demonstrates robust deepfake detection in IoT environments, indicating a reliable method for securing multimedia communication.

Key Points

  • The research aims to develop a reliable method for detecting deepfakes in multimedia communications using advanced machine learning techniques.
  • Utilized data augmentation, image resizing, and normalization to improve image quality.
  • Integrated feature fusion from ResNet-50, EfficientNet-B4, and MobileNetV3 for feature representation.
  • Employed adaptive hidden semi-Markov model to capture temporal dependencies in data.
  • Implemented a deep belief network for effective classification of real and fake content.
  • Achieved an impressive detection accuracy of 98.61% using the Celeb DF (v2) dataset.
  • Demonstrated improved generalization for distinguishing real from fake multimedia content.

Cite This Study

Jawhara Aljabri (2026) studied this question.

synapsesocial.com/papers/69bb91c7496e729e6297f300https://doi.org/10.1007/s44443-026-00654-1
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