Source Cell-Phone Identification Using Spectral Features of Device Self-noise

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• 关键词：Audio forensics ; Source cell ; phone identification ; Self ; noise ; Noise estimation ; Spectral features
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10082
• 期：1
• 页码：29-45
• 丛书名：Digital Forensics and Watermarking
• ISBN：978-3-319-53465-7
• 卷排序：10082

文摘

Source cell-phone identification has become a hot topic in multimedia forensics recently. In this paper, we propose a novel cell-phone identification method based on the recorded speech files. Device self-noise is considered as the fingerprint of the cell-phone, and the self-noise is estimated from the near-silent segments of recording. Moreover, two categories of spectral features of self-noise, i.e., spectral shape features (SN-SSF) and spectral distribution features (SN-SDF), are extracted for closed-set classification using SVM classifier. Experimental results show that the self-noise has the ability to identify the cell-phones of 24 different models, and identification accuracies of 89.23% and 94.53% have been obtained for SN-SSF and SN-SDF, respectively. To the best of our knowledge, it is the first attempt to comprehensively define the self-noise of cell-phone and furthermore apply it to source identification issue of audio forensics.