High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

This paper proposes a novel high capacity audio watermarking algorithm to embed data and extract them in a bit-exact manner by changing some of the magnitudes of the FFT spectrum. The key idea is to divide the FFT spectrum into short frames and change the magnitudes of the selected FFT samples using linear regression and the average of the samples of each frame. Using the average of FFT magnitudes leads to improved robustness, since this variable is more invariant against manipulations compared to the magnitudes of single samples. In addition, linear regression helps to minimize the alterations of FFT samples, which results in better transparency. Apart from very remarkable capacity, transparency and robustness, this scheme provides three parameters which facilitate the regulation of these properties. The experimental results show that the method has a high capacity (0.5 to 2.3 kbps), without significant perceptual distortion (ODG is about ¿1) and provides robustness against common audio signal processing such as echo, added noise, filtering and MPEG compression (MP3).