A Detection Algorithm of Audio Spread Spectrum Data Hiding

In this paper, a method of passive steganalysis is proposed. We focus on detecting the existing of data hidden in audio files with spread spectrum (SS) data hiding. SS data hiding is considered as a process of adding noise. The technology of classifier and feature vector extraction are used to achieve the detection. First, we divide an audio signal into several frames. The wavelet coefficients before and after wavelet de-noise in each frame are calculated. Then, we pick some stat, of their difference as the feature vectors of the audio signal. Finally, according to the feature vectors of the audio signal, classifier will decide whether the audio signal have been processed by SS or not. In our experiment, support vector machines (SVM) play role of classifier, 600 audio files are used to be our experiment samples. After the feature vectors of all the samples are calculated, those feature vectors of samples are divided into two parts. One is testing part and the other is training part. The result of experiment shows that if the strength of data hiding is higher than 0.005, the rate of correct detection of training part is higher than 86.5% and the rate of correct detection of testing part is higher than 82.5%.