Predicting embedding strength in audio steganography

As a serious concern of information security, steganography provides a covert communication channel for cyber-terrorism and cyber-crime. The widespread use enables MP3 compressed audio to become an excellent carrier for audio steganography on the Internet. Since embedding capacity is an important measure to evaluate the performance of steganographic systems, and embedding ratio is commonly used when comparing the accuracy of different steganalysis algorithms. In this paper, we present a scheme to predict embedding strength based on feature mining in MDCT transform domain. We generate reference signals by reversing and repeating quantification process, and derive the reference based accumulative features from the difference between source signal and reference signal. Finally, a dynamic evolving neuron-fuzzy inference system is applied to predict embedding strength of MP3 compressed audio. Experimental results show that our approach obtains good prediction of the embedding strength in the steganograms.