Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Increasing Robustness of LSB Audio Steganography Using a Novel Embedding Method
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
A new adaptive digital audio watermarking based on support vector machine
Journal of Network and Computer Applications
A Novel Synchronization Invariant Audio Watermarking Scheme Based on DWT and DCT
IEEE Transactions on Signal Processing
Robust and high-quality time-domain audio watermarking based on low-frequency amplitude modification
IEEE Transactions on Multimedia
Histogram-Based Audio Watermarking Against Time-Scale Modification and Cropping Attacks
IEEE Transactions on Multimedia
A pseudo-Zernike moment based audio watermarking scheme robust against desynchronization attacks
Computers and Electrical Engineering
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Protecting the intellectual property rights (IPR) of digital media has become an important issue. In this paper, counter-propagation neural networks (CPN) are applied to audio copyright protection. The db4 filter of the Daubechies wavelet is used on the original audio signals. The coefficients obtained from the 4-level Daubechies (db4) filter and the corresponding copyright information are used to train the CPN. Since the low-frequency components of DWT are robust, most noise is excluded. The CPN has memory and fault tolerance, so most attacks do not degrade the quality of the extracted copyright image. Moreover, the copyright generation procedure and the verification procedure are integrated into the proposed CPN method. Experimental results show that the proposed CPN-based method is robust, inaudible, and authentic.