Digital audio forensics: a first practical evaluation on microphone and environment classification

ABSTRACT

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.

REFERENCES

• 1: C. Borgelt, H. Timm, and R. Kruse. Probabilistic networks and fuzzy clustering as generalizations of naive bayes classifiers. In B. Reusch and K. -H. Temme, editors, Computational Intelligence in Theory and Practice (Advances in Soft Computing), pages 121--138. Physica-Verlag, Heidelberg, Germany, Heidelberg, Germany, 2001.
• 2: J. Dittmann, D. Hesse, and R. Hillert. Steganography and steganalysis in Voice over IP scenarios: Operational aspects and first experiences with a new steganalysis tool set. In E. J. D. III and P. W. Wong, editors, Security, Steganography, and Watermarking of Multimedia Contents VII, Electronic Imaging Science and Technology, pages 607--618, San Jose, California, USA, 2005. SPIE and IS&T, SPIE. ISBN 0-8194-5654-3.
• 3: J. Fridrich, J. Lukas, and M. Goljan. Digital camera identification from sensor noise. In IEEE Transactions on Information Security and Forensics, June 2006, Vol. 1(2), pages 205--214. IEEE, 2006.
• 4: David J. Hand , Padhraic Smyth , Heikki Mannila, Principles of data mining, MIT Press, Cambridge, MA, 2001
• 5: 2007. Andrew Moore: K-means and Hierarchical Clustering - Tutorial Slides, http://www-2.cs.cmu.edu/ awm/tutorials/kmeans.html.
• 6: N. Khanna, A. K. Mikkilineni, A. F. Martone, G. N. Ali, G. T. -C. Chiu, J. P. Allebach, and E. J. Delp. A survey of forensic characterization methods for physical devices. In Proceedings of the 6th Digital Forensics Research Workshop (DFRWS), Lafayette, Indiana, USA, August 2006, 17--28, 2006.
• 7: C. Kraetzer and J. Dittmann. Mel-cepstrum based steganalysis for voip-steganography. In E. J. Delp and P. W. Wong, editors, Security, Steganography, and Watermarking of Multimedia Contents IX, Electronic Imaging Science and Technology, SPIE Vol. 6505, San Jose, CA, USA, 2007. SPIE and IS&T, SPIE.
• 8: C. Kraetzer, J. Dittmann, and A. Lang. Transparency benchmarking on audio watermarks and steganography. In SPIE conference, at the Security, Steganography, and Watermarking of Multimedia Contents VIII, IS&T/SPIE Symposium on Electronic Imaging, 15-19th January, 2006, San Jose, USA, 2006.
• 9: J. Lukas, J. Fridrich, and M. Goljan. Determining digital image origin using sensor imperfections. In Proceedings of the SPIE International Conference on Security, Steganography, and Watermarking of Multimedia Contents VII, IS&T/SPIE Symposium on Electronic Imaging, 16-20th January, 2005, San Jose, USA, pages 249--260, 2005.
• 10: J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, pages 281--297. Berkeley, University of California Press, 1967.
• 11: A. Mikkilineni, O. Arslan, P. -J. Chiang, R. Kumontoy, J. Allebach, and G. -C. Chiu. Printer forensics using SVM techniques. In Proceedings of the IS&Ts NIP21: International Conference on Digital Printing Technologies, Baltimore, USA, October 2005, Vol. 21, pages 223--226, 2005.
• 12: A. K. Mikkilineni, P. -J. Chiang, G. N. Ali, G. T. C. Chiu, J. P. Allebach, and E. J. Delp. Printer identification based on graylevel co-occurrence features for security and forensic applications. In Proceedings of the SPIE International Conference on Security, Steganography, and Watermarking of Multimedia Contents VII, IS&T/SPIE Symposium on Electronic Imaging, 16-20th January, 2005, San Jose, USA, pages 430--440, 2005.
• 13: Andrea Oermann , Andreas Lang , Jana Dittmann, Verifier-tuple for audio-forensic to determine speaker environment, Proceedings of the 7th workshop on Multimedia and security, August 01-02, 2005, New York, NY, USA [doi>10.1145/1073170.1073181]
• 14: A. Oermann, C. Vielhauer, and J. Dittmann. Digitale Handschrift: Extraktion Gerätespezifischer Merkmale. In Proceedings of D-A-CH Security 2007, 2007.
• 15: A. Oermann, C. Vielhauer, and J. Dittmann. Identifying pen digitizers by statistical multimedia signal processing. In Proceedings of SPIE Electronic Imaging - Multimedia on Mobile Devices III, number SPIE Vol. 6507 in Electronic Imaging Science and Technology, San Jose, California, USA, 2007. SPIE and IS&T, SPIE.
• 16: G. T. Waters. Sound quality assessment material recordings for subjective tests. Users' handbook for the EBU - SQAM compact disc, European Broadcasting Union, Avenue Albert Lancaster 32, 1180 Bruxelles (Belgique), 1988.
• 17: 2007. WEKA 3http://www.cs.waikato.ac.nz/ml/weka/.
• 18: Ian H. Witten , Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems), Morgan Kaufmann Publishers Inc., San Francisco, CA, 2005

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Signal processing

General Terms:
Experimentation, Measurement, Reliability, Verification

Keywords:
digital media forensics, multimedia authentication