Analysis of smartphone model identification using digital images

Biney, Akua G. and Sellahewa, Harin (2013) Analysis of smartphone model identification using digital images. In: 20th IEEE International Conference on Image Processing, 15 - 18 Sep 2015, Melbourne, Australia.

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Official URL: http://doi.org/10.1109/ICIP.2013.6738924

Abstract

This paper is focused on smartphone model identification using image features. A total of 64 image features - broadly categorized into colour features, wavelet features and image quality features - are extracted from high-resolution smartphone images. A binary-class turned to multiclass support vector machine (SVM) is used as the classifier. Experimental results based on 1800 images captured with 10 different smartphone/tablet devices are promising in correctly identifying source smartphone model. Image quality metrics and wavelet features are shown to contain the most useful device/model information compared to colour features. However, compared to colour features, quality and wavelet features are highly sensitive to simple image modifications. The combined set of colour, quality and wavelet features achieves the overall best identification accuracy.

Item Type: Conference or Workshop Item (Poster)
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Smartphones, Wavelets (Mathematics), Support vector machines
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: School of Science > Applied Computing
Depositing User: Harin Sellahewa
Date Deposited: 28 Jul 2015 15:04
Last Modified: 07 Jun 2016 10:28
URI: http://bear.buckingham.ac.uk/id/eprint/33

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