Face Detection: Deepfake Face Detection Using the MIML Algorithm: A Meta-Learning Approach

Kartik Raut; Harsh Shrivas | Ishika Mendhekar | Chandrakant Kottalwar | Govind Raut; Harsh Pendke

Harsh Pendke | Kartik Raut | Harsh Shrivas | Ishika Mendhekar | Chandrakant Kottalwar | Govind Raut "Face Detection: Deepfake Face Detection Using the MIML Algorithm: A Meta-Learning Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025, pp.1018-1023, URL: https://www.ijtsrd.com/papers/ijtsrd76250.pdf

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Abstract :
The rise of deepfake technology, powered by artificial intelligence and deep learning, presents a major challenge to digital content authenticity. Conventional detection methods depend on deep learning models trained on specific datasets, but these models often struggle to identify previously unseen forgery techniques. This paper introduces an innovative deepfake detection framework integrating Multi-Instance Multi-Label (MIML) learning with meta-learning strategies. The proposed model leverages meta-learning to adapt to novel forgery techniques using limited training data, enhancing generalization. Experimental evaluation using the FaceForensics++ dataset demonstrates that our approach surpasses existing methods, improving accuracy and robustness while significantly reducing false positive rates.

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Publication Details:

Unique Identification Number : IJTSRD76250

Published In : Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025

Page Number(s) : 1018-1023

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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