Welcome to IJSDR UGC CARE norms ugc approved journal norms IJRTI Research Journal | ISSN : 2455-2631
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Issue: March 2023

Volume 8 | Issue 3

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
Authors Name: Binnar Nikita , Gaikwad Tejaswini , Naik Ravina , Sadgir Sarita
Unique Id: IJSDR2212100
Published In: Volume 7 Issue 12, December-2022
Abstract: Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required., we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23
Keywords: Convolutional neural networks; neural networks; forgery detection; image compression; image processing
Cite Article: "DETECTION OF RECOLORING AND COPY-MOVE FORGERY IN DIGITAL IMAGES", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 12, page no.657 - 659, December-2022, Available :http://www.ijsdr.org/papers/IJSDR2212100.pdf
Downloads: 000201534
Publication Details: Published Paper ID: IJSDR2212100
Registration ID:203068
Published In: Volume 7 Issue 12, December-2022
DOI (Digital Object Identifier):
Page No: 657 - 659
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

Click Here to Download This Article

Article Preview

Click here for Article Preview

Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
DOI (A digital object identifier)

Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media

Indexing Partner