UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 6
June-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2106406


Registration ID:
310471

Page Number

d43-d47

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Title

Copy Move Forgery Detection Using CNN on Pre-Trained Model: BusterNet

Abstract

With advancing technologies and plethora of editing tools it has become cumbersome to differentiate between real image and forged image. We introduce a Deep Neural Network for detecting passive image forgery. More precisely, this model is used for Copy Move Forgery Detection i.e. CMFD. Copy-move forgery in images is the most popular tampering method in which a portion of an image is copied and pasted in some other location of the same image. The architecture addresses two major limitations of older algorithms, firstly, it is end to end detection and secondly it produces source and target masks more accurately with varying threshold values. In recent years many models are being developed to detect forgery, In our model we have changed many existing things either by upgrading old data to newer version or by adding new technology to increase the efficiency of model. For example we will be using new libraries of Tensorflow and Keras to build whole model, also we will be adding some more data sets available publicly or from our side. We will change the pre existing values of data to newer one like standardization pixels of images from data set with the help of Convulational Neural Network. Experiment results are expected to change by developing new change in pre-existing model.

Key Words

CNN, Image forgery, pixels, CMFD, CASIA, Data sets

Cite This Article

"Copy Move Forgery Detection Using CNN on Pre-Trained Model: BusterNet", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.d43-d47, June-2021, Available :http://www.jetir.org/papers/JETIR2106406.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Copy Move Forgery Detection Using CNN on Pre-Trained Model: BusterNet", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppd43-d47, June-2021, Available at : http://www.jetir.org/papers/JETIR2106406.pdf

Publication Details

Published Paper ID: JETIR2106406
Registration ID: 310471
Published In: Volume 8 | Issue 6 | Year June-2021
DOI (Digital Object Identifier):
Page No: d43-d47
Country: PUNE, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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