Decode-Time Forensic Watermarking of AAC Bitstreams

In digital rights-management systems, forensic watermarking complements encryption and deters the capture and unauthorized redistribution of the rendered content. In this paper, we propose a novel watermarking method which is integrated into the advanced audio coding (AAC) standard's decoding process. For predefined frequency bands, the method intercepts and modifies the scale factors, which are utilized for dequantization of spectral coefficients. It thereby modulates the short-time envelope of the bandlimited audio and embeds a watermark which is robust to various attacks, such as capture with a microphone and recompression at lower bit rates. Inclusion of watermark embedding in the AAC decoder has practically no effect on the decoding complexity. As a result, the proposed method can be integrated even into resource-constrained devices, such as portable players without any additional hardware.     

A pattern recognition framework to blind audio watermark decoding

Conventional blind audio watermark (WM) decoders use matched-filtering techniques because of their simplicity. In these methods, WM decoding and WM detection are often considered as separate problems and the WM signal embedded by spreading a secret key through the spectrum of a host signal is extracted by maximizing correlation between the secret key and the received audio. Conventionally decoding is achieved by using a pre-defined decoding/detection threshold and tradeoff between the false rejection ratio and false acceptance ratio constitutes main drawback of the conventional decoders. Unlike the conventional methods, this paper introduces a pattern recognition (PR) framework to WM extraction and integrates WM decoding and detection problems into a unique classification problem that eliminates thresholding. The proposed method models statistics of watermarked and original audio signals by a Gaussian mixture model (GMM) with K components. Learning of the embedded WM data is achieved in a principal component analysis (PCA) transformed wavelet space and a maximum likelihood (ML) classifier is designed for WM decoding. Robustness of the proposed method is evaluated under compression, additive noise and Stirmark benchmark attacks. It is shown that both WM decoding and detection performances of the introduced decoder outperform the conventional decoders.