Source camera identification: a distributed computing approach using Hadoop

Source camera identification: a distributed computing approach using Hadoop The widespread use of digital images has led to a new challenge in digital image forensics. These images can be used in court as evidence of criminal cases. However, digital images are easily manipulated which brings up the need of a method to verify the authenticity of the image. One of the methods is by identifying the source camera. In spite of that, it takes a large amount of time to be completed by using traditional desktop computers. To tackle the problem, we aim to increase the performance of the process by implementing it in a distributed computing environment. We evaluate the camera identification process using conditional probability features and Apache Hadoop. The evaluation process used 6000 images from six different mobile phones of the different models and classified them using Apache Mahout, a scalable machine learning tool which runs on Hadoop. We ran the source camera identification process in a cluster of up to 19 computing nodes. The experimental results demonstrate exponential decrease in processing times and slight decrease in accuracies as the processes are distributed across the cluster. Our prediction accuracies are recorded between 85 to 95% across varying number of mappers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cloud Computing Springer Journals

Source camera identification: a distributed computing approach using Hadoop

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by The Author(s).
Subject
Computer Science; Computer Communication Networks; Special Purpose and Application-Based Systems; Information Systems Applications (incl.Internet); Computer Systems Organization and Communication Networks; Computer System Implementation; Software Engineering/Programming and Operating Systems
eISSN
2192-113X
D.O.I.
10.1186/s13677-017-0088-x
Publisher site
See Article on Publisher Site

Abstract

The widespread use of digital images has led to a new challenge in digital image forensics. These images can be used in court as evidence of criminal cases. However, digital images are easily manipulated which brings up the need of a method to verify the authenticity of the image. One of the methods is by identifying the source camera. In spite of that, it takes a large amount of time to be completed by using traditional desktop computers. To tackle the problem, we aim to increase the performance of the process by implementing it in a distributed computing environment. We evaluate the camera identification process using conditional probability features and Apache Hadoop. The evaluation process used 6000 images from six different mobile phones of the different models and classified them using Apache Mahout, a scalable machine learning tool which runs on Hadoop. We ran the source camera identification process in a cluster of up to 19 computing nodes. The experimental results demonstrate exponential decrease in processing times and slight decrease in accuracies as the processes are distributed across the cluster. Our prediction accuracies are recorded between 85 to 95% across varying number of mappers.

Journal

Journal of Cloud ComputingSpringer Journals

Published: Aug 15, 2017

References

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