A simple unpredictable pseudo random number generator
SIAM Journal on Computing
IBM Systems Journal
Robust audio watermarking using perceptual masking
Signal Processing
Digital watermarking based on neural networks for color images
Signal Processing - Special section on digital signal processing for multimedia communications and services
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on MPEG/Audio Compression
IEEE MultiMedia
On the Generation of Cryptographically Strong Pseudo-Random Sequences
Proceedings of the 8th Colloquium on Automata, Languages and Programming
On Resolving Rightful Ownership's of Digital Images by Invisible Watermarks
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
A multiresolution watermark for digital images
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Modified Patchwork Algorithm: A Novel Audio Watermarking Scheme
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Digital Watermarks for Audio Signals
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Robust audio watermarking in the time domain
IEEE Transactions on Multimedia
On the optimal design of fuzzy neural networks with robust learningfor function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
An SVD-based image watermarking in wavelet domain using SVR and PSO
Applied Soft Computing
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Based the characteristics of the human auditory system (HAS) and the techniques of neural networks, this work proposes an Adaptive Signal-Dependent Audio Watermarking (ASDAW) technique for protecting audio copyrights. First, a signal-dependent watermark for the ASDAW technique is generated by using the characteristics of the HAS (the temporal and frequency maskings). Next, the signal-dependent watermark is hidden in an original audio on the temporal domain. The ASDAW technique can make the signal-dependent audio watermark imperceptive (inaudible) because of the characteristics of the HAS. Moreover, an artificial neural network (ANN) is trained in the ASDAW technique so that the ASDAW technique can memorize the relationships between an original audio and the corresponding watermarked audio. Using the trained ANN (TANN), the ASDAW technique can extract the signal-dependent watermarks without the original audio. The extracted watermarks are then exploited in verifying legal duplications made of an audio during audio authentication. Consequently, the copyright forgery for audio can be suppressed greatly. Furthermore, experimental results illustrate that the ASDAW technique significantly possesses memorized, adaptive, and robust capabilities, making it immune against common audio manipulations and pirate attacks for counterfeiting audio copyrights.