Adaptive Audio Watermarking via the Optimization Point of View on the Wavelet-Based Entropy

TL;DR

This paper introduces an adaptive audio watermarking technique based on wavelet-based entropy that enhances robustness and audio quality by leveraging properties of wavelet coefficients and entropy measures.

Contribution

It presents a novel adaptive watermarking method utilizing wavelet-based entropy and characteristic curves for improved robustness and audio quality in watermark embedding and detection.

Findings

Robust against re-sampling, MP3 compression, filtering, and amplitude scaling.

Optimized watermark quality with high signal-to-noise ratio.

Effective extraction using only WBE values.

Abstract

This study aims to present an adaptive audio watermarking method using ideas of wavelet-based entropy (WBE). The method converts low-frequency coefficients of discrete wavelet transform (DWT) into the WBE domain, followed by the calculations of mean values of each audio as well as derivation of some essential properties of WBE. A characteristic curve relating the WBE and DWT coefficients is also presented. The foundation of the embedding process lies on the approximately invariant property demonstrated from the mean of each audio and the characteristic curve. Besides, the quality of the watermarked audio is optimized. In the detecting process, the watermark can be extracted using only values of the WBE. Finally, the performance of the proposed watermarking method is analyzed in terms of signal to noise ratio, mean opinion score and robustness. Experimental results confirm that the embedded data are robust to resist the common attacks like re-sampling, MP3 compression, low-pass filtering, and amplitude-scaling