Proceedings of the 11th ACM workshop on Multimedia and security
Exposing digital audio forgeries in time domain by using singularity analysis with wavelets
Proceedings of the first ACM workshop on Information hiding and multimedia security
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Media forensics tries to determine the originating device of a signal. We apply this paradigm to microphone forensics, determining the microphone model used to record a given audio sample. Our approach is to extract a Fourier coefficient histogram of near-silence segments of the recording as the feature vector and to use machine learning techniques for the classification. Our test goals are to determine whether attempting microphone forensics is indeed a sensible approach and which one of the six different classification techniques tested is the most suitable one for that task. The experimental results, achieved using two different FFT window sizes (256 and 2048 frequency coefficients) and nine different thresholds for near-silence detection, show a high accuracy of up to 93.5% correct classifications for the case of 2048 frequency coefficients in a test set of seven microphones classified with linear logistic regression models. This positive tendency motivates further experiments with larger test sets and further studies for microphone identification.