The impact of information fusion in steganalysis on the example of audio steganalysis
Christian Kraetzer, Jana Dittmann · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Information fusion tries to determine the best set of experts in a given problem domain and devise an appropriate function that can optimally combine the decisions of the individual experts. Only few systematic approaches to information fusion exist so far in the signal processing field of steganalysis. Under the basic assumption that steganalysis can be seen as a statistical pattern recognition process like biometrics, a state of the art five level information fusion model known from biometrics is transferred to steganalysis as well as statistical detectability evaluations for watermarking algorithms and its applicability is evaluated in practical testing. The primary test goal for these evaluations is to measure the impact of fusion on the classification accuracy. Therefore a match and decision level fusion are performed here for three selected data hiding algorithms (one steganography and two watermarking), two feature extractors and five different classifiers. For the test heterogeneous audio test sets are used for content independent training and testing. The secondary test goal of this work is to consider the impact of the key selection assumption on the accuracy of the classification in steganalysis. The results show for the test cases an increase of the classification accuracy for two of the three tested algorithms by match level fusions, no gain by decision level fusion and a considerably small impact of the key selection assumption on the statistical detectability.