ROBUST AUDIO WATERMARK DECODING BY NONLINEAR CLASSIFICATION (ThuAmPO2)

Author(s) : Serap Kirbiz (Istanbul Technical University, Turkey) Yusuf Yaslan (Istanbul Technical University, Turkey) Bilge Gunsel (Istanbul Technical University, Turkey)

Abstract : This paper introduces an audio watermark (WM) decoding scheme that performs a Support Vector Machine (SVM) based supervised learning followed by a blind decoding. The decoding process is modelled as a two-class classifica-tion procedure. Initially, wavelet decomposition is per-formed on the training audio signals, and the decomposed audio frames watermarked with +1 and -1 constitute the training sets for Class 1 and Class 2, respectively. The de-veloped system enables to extract embedded WM data at lower than -40dB Watermark-to-Signal-Ratio (WSR) lev-els with more than 95% accuracy and it is robust to degra-dations including audio compression (MP3, AAC), and additive noise. It is shown that the proposed audio WM decoder eliminates the drawbacks of correlation-based methods.

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