C4.5: programs for machine learning
C4.5: programs for machine learning
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Digital audio forensics: a first practical evaluation on microphone and environment classification
Proceedings of the 9th workshop on Multimedia & security
Proceedings of the 10th ACM workshop on Multimedia and security
Microphone Classification Using Fourier Coefficients
Information Hiding
Data mining and model trees study on GDP and its influence factors
AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
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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.