Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Random subspace method for aource camera identification

Tools
- Tools
+ Tools

Li, Ruizhe, Kotropoulos, C., Li, Chang-Tsun and Guan, Yu (2015) Random subspace method for aource camera identification. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP'15), Boston, USA, 17-20 Sept 2015. Published in: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)

[img]
Preview
PDF
WRAP_Random Subspace Method for Source Camera Identification.pdf - Accepted Version - Requires a PDF viewer.

Download (642Kb) | Preview
Official URL: http://dx.doi.org/10.1109/MLSP.2015.7324339

Request Changes to record.

Abstract

Sensor pattern noise is an inherent fingerprint of imaging devices, which has been widely used for source camera identification, image classification, and forgery detection. In a previous work, we proposed a feature extraction method based on the principal component analysis denoising concept, which can enhance the performance of conventional SPN extraction methods. However, this method is vulnerable, because the training samples are seriously affected by the image content. Accordingly, it is difficult to train a reliable feature extractor by using such a training set. To address this problem, a camera identification framework based on the random subspace method and majority voting is proposed in this work. The experimental results show that the proposed solution can suppress the interference from scene details and enhance the performance in terms of the receiver operating characteristic curve.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Digital cameras -- Identification, Image processing -- Digital techniques, Computer crimes -- Investigation, Pattern recognition systems
Journal or Publication Title: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)
Publisher: IEEE
Official Date: 12 July 2015
Dates:
DateEvent
6 June 2015Submitted
12 July 2015Accepted
Status: Peer Reviewed
Publication Status: Published
Date of first compliant deposit: 11 January 2016
Date of first compliant Open Access: 11 January 2016
Conference Paper Type: Paper
Title of Event: IEEE International Workshop on Machine Learning for Signal Processing (MLSP'15)
Type of Event: Workshop
Location of Event: Boston, USA
Date(s) of Event: 17-20 Sept 2015

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us