Adaptive Audio Steganography Based on Advanced Audio Coding and Syndrome-Trellis Coding

Abstract

Most existing audio steganographic methods embed secret messages according to a pseudorandom number generator, thus some auditory sensitive parts in cover audio, such as mute or near-mute segments, will be contaminated, which would lead to poor perceptual quality and may introduce some detectable artifacts for steganalysis. In this paper, we propose a novel adaptive audio steganography in the time domain based on the advanced audio coding (AAC) and the Syndrome-Trellis coding (STC). The proposed method firstly compresses a given wave signal into AAC compressed file with a high bitrate, and then obtains a residual signal by comparing the signal before and after AAC compression. According to the quantity and sign of the residual signal, \(\pm 1\) embedding costs are assigned to the audio samples. Finally, the STC is used to create the stego audio. The extensive results evaluated on 10,000 music and 10,000 speech audio clips have shown that our method can significantly outperform the conventional \(\pm 1\) LSB based steganography in terms of security and audio quality.