Unweighted fusion in microphone forensics using a decision tree and linear logistic regression models

For the exemplarily chosen domain of microphone forensics we show that media forensics can strongly benefit from combining statistical pattern recognition (using supervised classification) and unweighted information fusion (on the example of match-, rank- and decision level fusion). The practical results presented show that, by using a carefully selected fusion strategy and two multi-class classifiers (a decision tree and linear logistic regression models), the accuracy achieved in practical testing can be increased to 100%. This result is based on first tests on two sets of four and seven different microphones. For each of those microphones ten reference samples are recorded in ten different locations and are used in the ratio 80% to 20% for supervised training and testing by the two classifiers. The overall positive tendency indicates that microphone forensics might become an important security mechanism for the verification of source authenticity. Recent gunshot classification approaches, which try to determine the gun used in gunshot audio recordings, have the problem that they rely on carefully controlled conditions, amongst them the fact that the microphone used for all evaluations has to remain the same. A microphone classification approach as introduced here would allow for similarity estimation for microphones and thereby would enable exchanging microphones in such a gunshot classification approach without complete loss of confidence. Furthermore microphone forensics could be used in provenance verification of digital audio media to verify the microphone used for recordings to be submitted into secure long term archiving systems.