RES E A R C H Open Access
Source camera identification: a distributed
computing approach using Hadoop
, Nor Badrul Anuar
, Ainuddin Wahid Abdul Wahab
, Shahaboddin Shamshirband
and Anthony T. Chronopoulos
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.
Keywords: Source camera identification, Distributed computing, Hadoop, Mahout
As we live in an era of high technology, digital images
are commonly used due to the availability of various
models of digital cameras. Each day, more and more
digital cameras are invented by technology companies.
Consequently, digital cameras have become more afford-
able for the consumers to own. Mobile phones are now
equipped with digital cameras. This has further in-
creased the number of individuals owning image captur-
ing devices. As a consequence, thousands of images are
being created each day with some of them capturing a
critical moment in time such as a crime. These images
can be used in court as evidence to demonstrate the re-
lationship between the suspects and criminals .
However, a major issue in using digital images as
evidence in court is that digital images are easily created
and manipulated without leaving any obvious traces of
modifications. Evidence manipulation causes the credibility
and authenticity of the digital image to be questioned
. Therefore, we need more tools and applications
to address the problem of verifying the authenticity
of an image [12, 18].
Image authenticity is able to be verified through vari-
ous methods ranging from a simpler method like com-
paring the EXIF metadata method to a complex method
like tracing the digital fingerprints of the image. The lat-
ter seems to be more reliable and has attracted a grow-
ing interest among researchers in image forensics .
The digital fingerprints of an image provide distinguish-
ing characteristics of the image. Therefore, the forensic
analyzer is able to track the possible source camera of
the image under investigation whether it is acquired by
the device that it is claimed to be sensed with. Source
camera identification has been the focus of recent re-
search with various techniques being investigated .
There are a number of approaches in source camera
identification which are divided into two main categor-
ies: hardware and software-related [3, 9–11]. The
hardware-related approach is caused by defects in the
lens of the camera or any flaws in the device’s sensors.
In addition, hardware-related is further divided into two
* Correspondence: firstname.lastname@example.org
Department for Management of Science and Technology Development,
Ton Duc Thang University, Ho Chi Minh City, Vietnam
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh
Full list of author information is available at the end of the article
Journal of Cloud Computing:
Advances, Systems and Applications
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Faiz et al. Journal of Cloud Computing: Advances, Systems
and Applications (2017) 6:18