Robust Audio Watermarking in the Time Domain

The audio watermarking method presented below offers copyright protection to an audio signal by modifying its temporal characteristics. The amount of modification embedded is limited by the necessity that the output signal must not be perceptually different from the original one. The watermarking method presented here does not require the original signal for watermark detection. The watermark key is simply a seed known only by the copyright owner. This seed creates the watermark signal to be embedded. Watermark embedding depends also on the audio signal amplitude in a way that minimizes the audibility of the watermark signal. The embedded watermark is robust to MPEG audio coding, filtering, resampling and requantization. 1 Introduction The outstanding progress of digital technology has increased the ease with which digital data is reproduced and retransmitted. However, since the advantages of such a progress are broadly available, they offer equally increasing potential to both legal..

Robust Covert Communication over a Public Audio Channel Using Spread Spectrum

We present a set of watermarking techniques for effective covert communication over an audio signal carrier. Watermark robustness is enabled using: (i) redundant spread-spectrum for prevention against desynchronization attacks and (ii) psycho-acoustic frequency masking (PAFM). We show that PAFM impacts the balance of "ones" and "zeros" in the part of the spread-spectrum sequence used for watermark detection and propose a modified covariance test to compensate for that anomaly. The covert message is combined with the spread-spectrum sequence and additionally permuted in time to improve correlation convergence as well as the security of individual message bits. We have incorporated these techniques into a system capable of reliably detecting watermarks in audio that has been modified using a composition of attacks that degrade the original audio characteristics well beyond the limit of acceptable quality. Such attacks include: fluctuating scaling in time and frequency, editing, recompression, noise addition, resampling, normalization, and filtering