novel stream mining for audio steganalysis
In this paper, we present a novel stream data mining for audio steganalysis, based on second order derivative of audio streams. We extract Mel-cepstrum coefficients and Markov transition features on the second order derivative, a support vector machine is applied to the features for discovery of the existence of covert message in digital audios. We also explore the relation between signal complexity and detection performance on digital audios, which has not been studied previously. Our study shows that, in comparison with a recently proposed signal stream based Mel-cepstrum steganalysis, our method prominently improves the detection performance, which is not only related to information-hiding ratio but also signal complexity. Generally speaking, signal stream based Mel-cepstrum audio steganalysis performs well in steganalysis of audios with low signal complexity; it does not work so well on audios with high signal complexity. Our stream mining approach for audio steganalysis gains significant advantage in each category of signal complexity - especially in audios with high signal complexity, and thus improves the state of the art in audio steganalysis.
audio steganalysis second order derivative mel-cepstrum markov signal complexity svm spectrum steganography