Hybrid Deep Learning Approach for Deepfake Detection Using ResNet50 and EfficientNetB0
Year: 2025
Type: article
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.
Authors: Vaishnavi D., Jegan S., Ganesh J., Srinidhi Sundaram
Institution: SASTRA University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413193648