AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME

November 2025
Vol-11, Issue-6
Paper ID: 27740
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Engineering
Keywords
Deepfake detection On-device security Hybrid AI model Content authentication CNN-RNN Fusion Privacy Preservation Real-Time Video Analysis Edge Intelligence
Abstract
The rapid evolution of deepfake technology has intensified threats to the authenticity and security of live communication. While existing detection systems rely heavily on cloud-based processing, their latency and privacy limitations make them unsuitable for real-time use. This paper introduces an on-device, AI-powered framework that functions as a real-time deepfake guard for live video calls. The model integrates spatial analysis using convolutional neural networks (CNNs) and temporal analysis using recurrent neural networks (RNNs) with long short-term memory (LSTM) units to detect visual and behavioral inconsistencies such as unnatural lighting, motion irregularities, and lip-sync mismatches. Both analytical streams are fused to produce a Deepfake Probability score that enables instant alerts without cloud dependency. Emphasizing explainability, scalability, and privacy preservation, this work provides guidance toward developing efficient, interpretable, and real-time deepfake detection systems for secure digital communication.

Author Information

# Name Institute / Affiliation
1 Pavan Gajanan Bhonde Keystone School of Engineering Pune
2 Raj Sanjay Mane Keystone School of Engineering Pune
3 Ganesh Babasaheb Meher Keystone School of Engineering Pune
4 Prof. Tejal Rane Keystone School of Engineering Pune
5 Dr. Veena Kadam Keystone School of Engineering Pune

How to Cite

Use the following formats to cite this article in your research.

APA Style
Bhonde, Pavan Gajanan, Mane, Raj Sanjay, Meher, Ganesh Babasaheb, Rane, Prof. Tejal, & Kadam, Dr. Veena (2025). AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME. International Journal of Advance Research and Innovative Ideas In Education, 11(6), 803-809.
MLA Style
Bhonde, Pavan Gajanan, et al. "AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 6, 2025, pp. 803-809.
IEEE Style
Pavan Gajanan Bhonde, Raj Sanjay Mane, Ganesh Babasaheb Meher, Prof. Tejal Rane, and Dr. Veena Kadam, "AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 6, pp. 803-809, 2025.
Vancouver Style
Bhonde Pavan Gajanan, Mane Raj Sanjay, Meher Ganesh Babasaheb, Rane Prof. Tejal, Kadam Dr. Veena. AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(6):803-809.
Harvard Style
Bhonde, Pavan Gajanan, Mane, Raj Sanjay, Meher, Ganesh Babasaheb, Rane, Prof. Tejal, & Kadam, Dr. Veena (2025) 'AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME', International Journal of Advance Research and Innovative Ideas In Education, 11(6), pp. 803-809.
Chicago Style
Bhonde, Pavan Gajanan, et al. "AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME." International Journal of Advance Research and Innovative Ideas In Education 11, no. 6 (2025): 803-809.
Turabian Style
Bhonde, Pavan Gajanan, et al. "AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIME." International Journal of Advance Research and Innovative Ideas In Education 11, no. 6 (2025): 803-809.

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