DocumentCode :
569153
Title :
Camera Model Identification Using Local Binary Patterns
Author :
Xu, Guanshuo ; Shi, Yun Qing
Author_Institution :
New Jersey Inst. of Technol., Newark, NJ, USA
fYear :
2012
fDate :
9-13 July 2012
Firstpage :
392
Lastpage :
397
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 ´Dresden Image Database´. Compared with the results reported in literatures, the detection accuracy reported in this paper is higher.
Keywords :
cameras; image classification; image colour analysis; statistical analysis; support vector machines; wavelet transforms; 1st-level diagonal wavelet subbands; 8-neighbor binary co-occurrence; Dresden image database; LBP; camera model classification; camera model identification; digital image forensics; feature extraction; green color channels; multiclass support vector machines; prediction-error arrays; red color channels; source camera model information; spatial domain; statistical features; uniform gray-scale invariant local binary patterns; Accuracy; Cameras; Feature extraction; Image color analysis; Testing; Training; LBP; camera model identification; local binary patterns; source device identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo (ICME), 2012 IEEE International Conference on
Conference_Location :
Melbourne, VIC
ISSN :
1945-7871
Print_ISBN :
978-1-4673-1659-0
Type :
conf
DOI :
10.1109/ICME.2012.87
Filename :
6298429
Link To Document :
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