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Video camera identification using audio-visual features

 
: Milani, S.; Cuccovillo, L.; Tagliasacchi, M.; Tubaro, S.; Aichroth, P.

:

Tubaro, S. ; Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, France Section; European Association for Signal Processing -EURASIP-:
5th European Workshop on Visual Information Processing, EUVIP 2014 : Paris, France, 10 - 12 December 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-4571-9
ISBN: 978­-1­-4799­-4572-6
pp.265-270
European Workshop on Visual Information Processing (EUVIP) <5, 2014, Paris>
English
Conference Paper
Fraunhofer IDMT ()
device identification; image forensics; audio-visual analysis; CFA classification

Abstract
One of the major issues in multimedia forensics is the identification of video acquisition devices. Most of the relevant state-of-the-art solutions rely on either visual or audio analysis, using feature arrays that are highly correlated with the characteristics of the respective camera or microphone. In this work, we present a multi-modal approach that uses both video and audio information to improve the detection accuracy. For this purpose, microphone detection based on the blind estimation of the frequency response is complemented with a video camera detection based on a set of video features related to the Color Filter Array interpolation. Experimental results show that the combined approach results in an improved overall classification accuracy over the mono-modal cases.

: http://publica.fraunhofer.de/documents/N-345652.html