Camera model identification using local binary patterns

Document Type

Conference Proceeding

Publication Date

11-5-2012

Abstract

In digital image forensics, camera model identification seeks for the source camera model information from the given images under investigation. To achieve this goal, one of the popular approaches is extracting from the images under investigation certain statistical features that capture the difference caused by camera structure and various in-camera image processing algorithms, followed by machine learning and pattern recognition algorithms for similarity measures of extracted features. In this paper, we propose to use uniform gray-scale invariant local binary patterns (LBP) as statistical features. Considering 8-neighbor binary co-occurrence, three groups of 59 local binary patterns are extracted from the spatial domain of red and green color channels, their corresponding prediction-error arrays, and their 1st-level diagonal wavelet sub bands of each image, respectively. Multi-class support vector machines are built for classification of 18 camera models from âDresden Image Database'. Compared with the results reported in literatures, the detection accuracy reported in this paper is higher. © 2012 IEEE.

Identifier

84868151871 (Scopus)

Publication Title

Proceedings IEEE International Conference on Multimedia and Expo

External Full Text Location

https://doi.org/10.1109/ICME.2012.87

e-ISSN

1945788X

ISSN

19457871

First Page

392

Last Page

397

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