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Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm
S. Neha Nikhitha1 , R. Bhavya2 , K. Krishna Jyothi3 , G. Kalyani4
Section:Research Paper, Product Type: Journal
Vol.12 ,
Issue.2 , pp.27-30, Apr-2024
Online published on Apr 30, 2024
Copyright © S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani, “Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm,” International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.2, pp.27-30, 2024.
MLA Style Citation: S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani "Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm." International Journal of Scientific Research in Network Security and Communication 12.2 (2024): 27-30.
APA Style Citation: S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani, (2024). Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm. International Journal of Scientific Research in Network Security and Communication, 12(2), 27-30.
BibTex Style Citation:
@article{Nikhitha_2024,
author = {S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani},
title = {Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm},
journal = {International Journal of Scientific Research in Network Security and Communication},
issue_date = {4 2024},
volume = {12},
Issue = {2},
month = {4},
year = {2024},
issn = {2347-2693},
pages = {27-30},
url = {https://www.isroset.org/journal/IJSRNSC/full_paper_view.php?paper_id=445},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.isroset.org/journal/IJSRNSC/full_paper_view.php?paper_id=445
TI - Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm
T2 - International Journal of Scientific Research in Network Security and Communication
AU - S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani
PY - 2024
DA - 2024/04/30
PB - IJCSE, Indore, INDIA
SP - 27-30
IS - 2
VL - 12
SN - 2347-2693
ER -
Abstract :
The widespread use of digital image alteration emphasizes how urgently reliable detection methods are needed to protect the originality and integrity of visual content. In this paper, we present an original technique for copy-move image forgery detection that combines Scale-Invariant Feature Transform (SIFT) with Convolutional Neural Networks . Our approach uses SIFT for reliable key-point descriptor extraction and Error Level Analysis (ELA) preprocessing to improve potentially changed regions. In parallel, a CNN model is trained using characteristics extracted from ELA representations to distinguish between modified and unmanipulated images. Although our hybrid methodology shows promising results in identifying copy-move forgeries, it is important to recognize the limits of current methods and systems.These drawbacks include the inability to grasp the results, scalability problems, dependency on handcrafted characteristics, computational complexity, limited generalization, partial copy-move vulnerabilities, and lack of interpretability. Our suggested method`s incorporation of SIFT is essential for identifying forgeries, especially in situations where copy-move manipulation is involved. By offering robust and unique descriptors that are independent of scale, rotation, and translation, SIFT features provide precise recognition of replicated areas in a picture. This method improves the model`s capacity to identify minute changes and visualise the location of forgery by utilizing SIFT in conjunction with CNN. This helps to maintain the visual authenticity and reliability of digital content.
Key-Words / Index Term :
Copy-move,CNN,ELA,SIFT
References :
[1] K.H. Rhee, “ Generation of Novelty Ground Truth Image Using Image Classification and Semantic Segmentation for Copy-Move Forgery Detection”, IEEE access Vol.10, pp.2783-2796, 2022.
[2] S. P. Shyry, M. Saranya, M. Mahitha, “ Digital Image Forgery Detection”, Vol.8,Issue.2S3, pp.658-661, 2019.
[3] V. S. Dhania, K. H. Binu , “ Improving Digital Image Forgery Detection Using MIFT Features and Adaptive Over Segmentation”, International Research Journal Of Engineering And Technology(IRJET), Vol.3, Issue.7, pp.609-612, 2016.
[4] K. Jha, S. Jacob, “Digital Image Forgery Detection”, International Research Journal Of Engineering And Technology(IRJET), Vol.7, Issue.5, pp.4542-4547,2022.
[5] N. Kaur, N. Kanwal ,“Review And Analysis of Image Forgery Detection Technique for Digital Images”, International Journal of Advanced Research in Computer Science, Vol.8, Issue.5, pp.2700-2706, 2017.
[6] B. S. Kumar, S. Karthi, K. Karthika, and R. Cristin, “A Systematic Study of Image Forgery Detection”, Journal of Computational and Theoretical Nanoscience, Vol.15, No.8, pp.2560-2564, 2018.
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