Detection of audio covert channels using statistical footprints of hidden messages

Abstract

We address the problem of detecting the presence of hidden messages in audio. The detector is based on the characteristics of the denoised residuals of the audio file, which may consist of a mixture of speech and music data. A set of generalized moments of the audio signal is measured in terms of objective and perceptual quality measures. The detector discriminates between cover and stego files using a selected subset of features and an SVM classifier. The proposed scheme achieves on the average 88% discrimination performance on individual steganographic algorithms and 98.5% on individual watermarking algorithms. Between 75 and 90% discrimination performance is achieved in universal tests. Correct detection performance for individual embedding algorithms is roughly 90% when the detector can encounter any one in an ensemble of different embedding algorithms.