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.

近年来，源设备识别已成为多媒体取证领域的热门话题。本文提出了一种通过使用编码特性作为记录设备的固有指纹识别源智能手机的新方法。研究了来自7个主流品牌的24种流行型号的智能手机的编码特性，并提取了一些重要参数的统计特征作为智能手机识别的鉴别特征。为了在合理的特征尺寸和较高的分类率之间保持平衡，设计了由变异阈值和SVM-RFE组成的两步​​特征选择策略，以选择最佳特征。实验结果表明，该方法可以达到97.89％和98的高识别率。实时录制数据库（CKC-SD）和TIMIT重新捕获数据库（TIMIT-RSD）分别为04％，而且与使用录制语音的两种典型源识别方法相比，我们的方案性能更好。另外，在面对双重压缩攻击的同时，评估了所提出特征的鲁棒性。