IBM Systems Journal
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Chaos-based discrete fractional Sine transform domain audio watermarking scheme
Computers and Electrical Engineering
An adaptive audio watermarking based on the singular value decomposition in the wavelet domain
Digital Signal Processing
An intelligent watermarking method based on particle swarm optimization
Expert Systems with Applications: An International Journal
An audio watermarking scheme using singular value decomposition and dither-modulation quantization
Multimedia Tools and Applications
Blind and robust audio watermarking scheme based on SVD-DCT
Signal Processing
An SVD audio watermarking approach using chaotic encrypted images
Digital Signal Processing
Intelligent reversible watermarking in integer wavelet domain for medical images
Journal of Systems and Software
A Novel Synchronization Invariant Audio Watermarking Scheme Based on DWT and DCT
IEEE Transactions on Signal Processing
A New Adaptive Digital Audio Watermarking Based on Support Vector Regression
IEEE Transactions on Audio, Speech, and Language Processing
Robust and secure image hashing
IEEE Transactions on Information Forensics and Security
On the Selection of Optimal Feature Region Set for Robust Digital Image Watermarking
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Reversible watermarking scheme for medical image based on differential evolution
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Due to the development of the Internet, security and intellectual property protection have attracted significant interest in the copyright protection field recently. A novel watermarking scheme for breath sounds, combining lifting wavelet transform (LWT), discrete cosine transform (DCT), singular value decomposition (SVD) and dither modulation (DM) quantization is proposed in this paper as a way to insert encrypted source and identity information in breath sounds while maintaining significant biological signals. In the proposed scheme, LWT is first performed to decompose the signal, and then DCT is applied on the approximate coefficients. SVD is carried out on the LWT-DCT coefficients to derive singular values. DM is adopted to quantize the singular values of each of the LWT-DCT blocks; thus, the watermark extraction is blind by using the DM algorithm. The novelty of our proposed method also includes the introduction of the particle swarm optimization (PSO) technique to optimize the quantization steps for the DM approach. The experimental results demonstrate that the proposed watermarking scheme obtains good robustness against common manipulation attacks and preserves imperceptivity. The performance comparison results verify that our scheme outperforms existing approaches in terms of robustness and imperceptibility.