Principles of data mining
Verifier-tuple for audio-forensic to determine speaker environment
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Detecting digital audio forgeries by checking frame offsets
Proceedings of the 10th ACM workshop on Multimedia and security
Psycho-acoustic model-based message authentication coding for audio data
Proceedings of the 10th ACM workshop on Multimedia and security
Proceedings of the 11th ACM workshop on Multimedia and security
Exposing MP3 audio forgeries using frame offsets
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Issue on Multimedia Security
Automatic telephone handset identification by sparse representation of random spectral features
Proceedings of the 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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In this paper a first approach for digital media forensics is presented to determine the used microphones and the environments of recorded digital audio samples by using known audio steganalysis features. Our first evaluation is based on a limited exemplary test set of 10 different audio reference signals recorded as mono audio data by four microphones in 10 different rooms with 44.1 kHz sampling rate and 16 bit quantisation. Note that, of course, a generalisation of the results cannot be achieved. Motivated by the syntactical and semantical analysis of information and in particular by known audio steganalysis approaches, a first set of specific features are selected for classification to evaluate, whether this first feature set can support correct classifications. The idea was mainly driven by the existing steganalysis features and the question of applicability within a first and limited test set. In the tests presented in this paper, an inter-device analysis with different device characteristics is performed while intra-device evaluations (identical microphone models of the same manufacturer) are not considered. For classification the data mining tool WEKA with K-means as a clustering and Naive Bayes as a classification technique are applied with the goal to evaluate their classification in regard to the classification accuracy on known audio steganalysis features. Our results show, that for our test set, the used classification techniques and selected steganalysis features, microphones can be better classified than environments. These first tests show promising results but of course are based on a limited test and training set as well a specific test set generation. Therefore additional and enhanced features with different test set generation strategies are necessary to generalise the findings.