INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Image forgery is a critical issue in the field of image processing, and the detection of such forgeries is essential to ensure the authenticity and integrity of digital images. In this paper, we present a deep learning-based approach for image forgery detection using a combination of Convolutional Neural Networks (CNN) and Error Level Analysis (ELA). Our approach is capable of detecting various types of image forgeries, including copy-move forgery, splicing, retouching, and removal. We evaluate our approach on a publicly available dataset and achieve promising results, outperforming existing state-of-the-art techniques for image forgery detection. Our results demonstrate the potential of combining CNN and ELA for robust and accurate image forgery detection.
Keywords:
Digital Forensic, Digital image Manipulation, Error Level Analysis, Convolution neural network
Cite Article:
"Image Forgery Detection using Deep Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 6, page no.282 - 286, June-2023, Available :http://www.ijsdr.org/papers/IJSDR2306044.pdf
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Publication Details:
Published Paper ID: IJSDR2306044
Registration ID:205585
Published In: Volume 8 Issue 6, June-2023
DOI (Digital Object Identifier):
Page No: 282 - 286
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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