Home > Computer Science > Other > Volume-2 > Issue-3 > Anticipation of Forged Video Evidence using Machine Learning

Anticipation of Forged Video Evidence using Machine Learning

Call for Papers

Volume-8 | Issue-6

Last date : 27-Dec-2024

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area


Anticipation of Forged Video Evidence using Machine Learning


G. Abinaya | K. Sridevi Nattar | Dr. Rajini Girinath

https://doi.org/10.31142/ijtsrd11357



G. Abinaya | K. Sridevi Nattar | Dr. Rajini Girinath "Anticipation of Forged Video Evidence using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3, April 2018, pp.1429-1433, URL: https://www.ijtsrd.com/papers/ijtsrd11357.pdf

To detect audio manipulation in a pre recorded evidence videos by developing a synchronization verification algorithm to match the lip movements along with its audio pitch values. Audio video recognition has been considered as a key for speech recognition tasks when the audio is sullied, as well as visual recognition method used for speaker authentication in multispeaker scenarios. The primary aim of this paper is to point out the correspondence between the audio and video streams. Acquired audio feature sequences are processed with a Gaussian model. [1].This proposed method achieves parallel processing by effectively examining multiple videos at a time.In this paper, we train the machine by convolutional neural network (CNN) and deep neural network (DNN).CNN architecture maps both the modalities into a depiction space to evaluate the correspondence of audio –visual streams using the learned multimodal features. DNN is used as a discriminative model between the two modalities in order to concurrently distinguish between the correlated and uncorrelated components. The proposed architecture will deploy both spatial and temporal information jointly to effectively discover the correlation between temporal information for different modalities. We train a system by capturing the motion picture. This method achieves relative enhancement over 20% on the equal error rate and 7% on the average precision in comparison to the state of the art method.

Convolutional neural network, Audio-visual recognition, Deep neural network


IJTSRD11357
Volume-2 | Issue-3, April 2018
1429-1433
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.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin