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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Springer Verlag (Germany)
2017
|
| Subjects: |
| _version_ | 1825721250405679104 |
|---|---|
| author | Faiz, M. Anuar, N.B. Wahab, A.W.A. Shamshirband, S. Chronopoulos, A.T. |
| author_facet | Faiz, M. Anuar, N.B. Wahab, A.W.A. Shamshirband, S. Chronopoulos, A.T. |
| author_sort | Faiz, M. |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:47:32Z |
| format | Article |
| id | um.eprints-19185 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:47:32Z |
| publishDate | 2017 |
| publisher | Springer Verlag (Germany) |
| record_format | dspace |
| spelling | um.eprints-191852018-09-12T01:39:59Z http://eprints.um.edu.my/19185/ Source camera identification: a distributed computing approach using Hadoop Faiz, M. Anuar, N.B. Wahab, A.W.A. Shamshirband, S. Chronopoulos, A.T. QA75 Electronic computers. Computer science 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. Springer Verlag (Germany) 2017 Article PeerReviewed Faiz, M. and Anuar, N.B. and Wahab, A.W.A. and Shamshirband, S. and Chronopoulos, A.T. (2017) Source camera identification: a distributed computing approach using Hadoop. Journal of Cloud Computing, 6 (1). p. 18. ISSN 2192-113X, DOI https://doi.org/10.1186/s13677-017-0088-x <https://doi.org/10.1186/s13677-017-0088-x>. http://dx.doi.org/10.1186/s13677-017-0088-x doi:10.1186/s13677-017-0088-x |
| spellingShingle | QA75 Electronic computers. Computer science Faiz, M. Anuar, N.B. Wahab, A.W.A. Shamshirband, S. Chronopoulos, A.T. Source camera identification: a distributed computing approach using Hadoop |
| title | Source camera identification: a distributed computing approach using Hadoop |
| title_full | Source camera identification: a distributed computing approach using Hadoop |
| title_fullStr | Source camera identification: a distributed computing approach using Hadoop |
| title_full_unstemmed | Source camera identification: a distributed computing approach using Hadoop |
| title_short | Source camera identification: a distributed computing approach using Hadoop |
| title_sort | source camera identification a distributed computing approach using hadoop |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT faizm sourcecameraidentificationadistributedcomputingapproachusinghadoop AT anuarnb sourcecameraidentificationadistributedcomputingapproachusinghadoop AT wahabawa sourcecameraidentificationadistributedcomputingapproachusinghadoop AT shamshirbands sourcecameraidentificationadistributedcomputingapproachusinghadoop AT chronopoulosat sourcecameraidentificationadistributedcomputingapproachusinghadoop |