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A Meta-Learning Method for Few-Shot Face Forgery Segmentation and Classification

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A Meta-Learning Method for Few-Shot Face Forgery Segmentation and Classification


Govind Raut | Prof. Harshita | Chandrakant Kottalwar | Prof. Anupam Chaube



Govind Raut | Prof. Harshita | Chandrakant Kottalwar | Prof. Anupam Chaube "A Meta-Learning Method for Few-Shot Face Forgery Segmentation and Classification" 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.398-403, URL: https://www.ijtsrd.com/papers/ijtsrd75008.pdf

Detection of Forgeries in Images: A Survey Abstract: While the technology to detect forgeries in images is able to detect images even at complex images, this well is only limited to known forgery methods. It trains neural networks from large amounts of original and corresponding forged images created with known techniques. But it fails to process unseen forgery techniques. One such proposed solution to this problem, recently, is to employ a hand-crafted generator of forged images to generate a series of fake images and feed them to the neural network for training However, the aforementioned approach has certain limitations detecting performance in situations where the hand-craft generator has not taken into consideration invisible forging processes. In this study, we use a meta-learning approach to create a highly adaptive detector for detecting novel forging techniques, overcoming the drawbacks of current approaches. By employing meta-learning approaches to train a forged image detector, the suggested method allows the detector to be fine-tuned using a small number of fresh forged examples. In order to detect forged images with comparable characteristics, the suggested method inputs a limited number of the forged images to the detector and allows the detector to modify its weights based on the statistical properties of the input forged photos. With IoU gains ranging from 35.4% to 127.2%, the suggested approach significantly improves forgery method detection. These findings illustrate that the suggested approach outperforms the state-of-the-art techniques in the majority of situations and greatly enhances detection performance with a relatively small number of samples.

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IJTSRD75008
Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025
398-403
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)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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