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dc.contributor.authorTsai, Min-Jenen_US
dc.contributor.authorLai, Cheng-Liangen_US
dc.contributor.authorLiu, Jungen_US
dc.date.accessioned2014-12-08T15:09:11Z-
dc.date.available2014-12-08T15:09:11Z-
dc.date.issued2007en_US
dc.identifier.issn1520-6149en_US
dc.identifier.urihttp://hdl.handle.net/11536/7001-
dc.description.abstractDigital forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This study has focused on analyzing the relationship between digital cameras and the photographs by using the support vector machine (SVM). Based on the fact that the internal imaging formation algorithms of the cameras are different from one manufacturer to another, our approach first calculates the characteristic values of the images taken by different cameras in conjunction with image processing techniques and data exploration methods. The training and categorization procedures of the image characteristic values are further conducted through SVM to identify the source camera of the images. Based on SVM's ability to distinguish cameras of different brands, this study also examines whether the method can differentiate cameras of the same brand, or even the popular mobile phones with camera. The experiment results demonstrate that our approach can achieve higher identification rate for camera and mobile phone sources than the results from other literatures.en_US
dc.language.isoen_USen_US
dc.subjectcamerasen_US
dc.subjectcorrelationen_US
dc.subjectfeature extractionen_US
dc.titleCamera/mobile phone source identification for digital forensicsen_US
dc.typeArticleen_US
dc.identifier.journal2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol II, Pts 1-3en_US
dc.citation.spage221en_US
dc.citation.epage224en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000248908100056-
Appears in Collections:Conferences Paper