Abstract
Source device identification has become a hot topic in multimedia forensics recently. In this paper, a novel method is proposed for source smartphone identification by using encoding characteristics as the intrinsic fingerprint of recording devices. The encoding characteristics for the smartphones of 24 popular models derived from 7 mainstream brands are investigated and statistical features of some important parameters are extracted as the discriminative features for the smartphone identification. To keep a balance between reasonable feature dimension and high classification rate, a two-step feature selection strategy consisting of Variance Threshold and SVM-RFE is designed to choose the optimal features. Experimental results show that the proposed method can achieve high identification rates of 97.89% and 98.04% for the live recorded database (CKC-SD) and the TIMIT recaptured database (TIMIT-RSD), respectively, and furthermore our scheme performs better when compared with two typical source identification approaches using recorded speeches. In addition, robustness of the proposed features is evaluated while confronting double compression attack.
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