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Reseach Article

Improvements on Sensor Noise based on Source Camera Identification using GLCM

Published on February 2015 by Nilambari Kulkarni, Vanita Mane
International Conference on Advances in Science and Technology
Foundation of Computer Science USA
ICAST2014 - Number 4
February 2015
Authors: Nilambari Kulkarni, Vanita Mane
a8bcb3e6-3d77-4c93-9cf1-e60525048226

Nilambari Kulkarni, Vanita Mane . Improvements on Sensor Noise based on Source Camera Identification using GLCM. International Conference on Advances in Science and Technology. ICAST2014, 4 (February 2015), 1-4.

@article{
author = { Nilambari Kulkarni, Vanita Mane },
title = { Improvements on Sensor Noise based on Source Camera Identification using GLCM },
journal = { International Conference on Advances in Science and Technology },
issue_date = { February 2015 },
volume = { ICAST2014 },
number = { 4 },
month = { February },
year = { 2015 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/icast2014/number4/19490-5039/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Science and Technology
%A Nilambari Kulkarni
%A Vanita Mane
%T Improvements on Sensor Noise based on Source Camera Identification using GLCM
%J International Conference on Advances in Science and Technology
%@ 0975-8887
%V ICAST2014
%N 4
%P 1-4
%D 2015
%I International Journal of Computer Applications
Abstract

In the fields such as forensics, medical imaging, e-commerce, and industrial photography, authenticity and integrity of digital images is essential. Digital images are becoming prime focus of work for the researchers. Typical image forensics includes source device identification, source device linking, and classification of images taken by unknown cameras, integrity verification, and authentication. Source camera identification provides different techniques to identify the characteristics of the digital devices used. Study of these techniques has been done as literature survey work; from this sensor imperfection based technique is chosen. Sensor pattern noise (SPN), carries abundance of information along a wide frequency range allows for reliable identification in the presence of many imaging sensors. Our proposed system consists of a novel technique used for extracting sensor noise from the database images, and then the feature extraction method is applied to extract the features. The model used for extracting sensor noise consists of use of Gradient based operators and Laplacian operators, a hybrid system consisting of best results from the above two operators obtain a third image giving the edges and noise present in it. The edges are removed by applying threshold to get the noise present in the image. This noisy image is then provided to the feature extraction module consisting of Gray level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). A feature set of extracted features from the above techniques is obtained and used as the matching set for classification purpose. The KNN classifier is used for matching the images of test data set with the training dataset.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Forensics Source Camera Identification Pattern Noise.