Vol. 7 No. 3 (2025), Articles
Volume 7 — Issue 3 — September 2025

Hybrid Deep Learning Approach for Deepfake Detection Using ResNet50 and EfficientNetB0

Published 12-08-2025
https://doi.org/10.36548/jiip.2025.3.003
Vaishnavi D.
Department of CSE, SRC, SASTRA Deemed to be University
Jegan S.
Department of CSE, Teaching Fellow, University College of Engineering Villupuram
Ganesh J.
Department of CSE, SRC, SASTRA Deemed to be University
Srinidhi Sundaram
Department of AI and DS, Panimalar Engineering College, Chennai, India
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How to Cite

D., Vaishnavi, Jegan S., Ganesh J., and Srinidhi Sundaram. 2025. “Hybrid Deep Learning Approach for Deepfake Detection Using ResNet50 and EfficientNetB0”. Journal of Innovative Image Processing 7 (3): 622-38. https://doi.org/10.36548/jiip.2025.3.003.
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Keywords

  • Haar Cascade
  • Artificial Intelligence
  • Transfer Learning
  • Deep Learning
  • Facial Recognition
  • Synthetic Media

Abstract

Deepfake is an environment in which AI technology is used to manipulate original digital videos, making them appear real. This poses a serious issue regarding the trustworthiness of digital data. The goal of the study is to create a reliable method to detect deepfakes using enhanced learning models named EfficientB0 and RESNET50. Frames are extracted from videos, and a HAAR cascade is applied to locate the face region, which is then sent as a dataset to train the model. This study utilized an open dataset from Kaggle to perform the experiment. The performance of the study is quantitatively measured using F1-score, accuracy, recall, and precision. The experiment showed that the hybrid model achieved superior prediction results of 89.08% compared to the standalone models. Hence, this study confirms that the proposed model works well to identify fake videos, which may help increase trust in digital data.

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