Watermarking of uncompressed and compressed video
Signal Processing
The lifting scheme: a construction of second generation wavelets
SIAM Journal on Mathematical Analysis
Robust audio watermarking using perceptual masking
Signal Processing
Proceedings of the First International Workshop on Information Hiding
Digital Watermarks for Audio Signals
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Audio Watermarking Algorithm Based on Wavelet Packet and Psychoacoustic Model
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
New echo embedding technique for robust and imperceptible audio watermarking
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Robust spread-spectrum audio watermarking
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Robust multi bit and high quality audio watermarking using pseudo-random sequences
Computers and Electrical Engineering
A temporal domain audio watermarking technique
IEEE Transactions on Signal Processing
Robust audio watermarking in the time domain
IEEE Transactions on Multimedia
Time-spread echo method for digital audio watermarking
IEEE Transactions on Multimedia
Audio watermarking techniques using sinusoidal patterns based on pseudorandom sequences
IEEE Transactions on Circuits and Systems for Video Technology
An adaptive audio watermarking based on the singular value decomposition in the wavelet domain
Digital Signal Processing
A robust digital audio watermarking scheme using wavelet moment invariance
Journal of Systems and Software
A robust content based audio watermarking using UDWT and invariant histogram
Multimedia Tools and Applications
Genetic swarm based robust image watermarking
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Hi-index | 0.00 |
With the recent development of information technology and computer network, digital format of data has become more and more popular. However, a major problem faced by digital data providers and owners is protecting data from unauthorized copying and distribution. As a solution to the problem, digital watermark technology is now attracting attention as new method of protection against said unauthorized copying and distribution. The aim of the digital audio watermarking is to take prespecified data that carries certain information and hide it within the audio stream such that it is not audible to the human ear (i.e., transparent) but at the same time renders the file more resistant to removal (i.e., robust). In this paper, we propose a new method for embedding digital watermarks into audio signals in low frequency components, which method mitigates these and other related shortcomings. The proposed method uses the wavelet transform constructed by lifting-based wavelet transform (LBWT) in order to provide a fast implementation between watermark embedding and extraction parts. In the first stage of the proposed method, the original audio host signal is converted to a wavelet domain using LBWT. The signal is thus decomposed into low and high frequency components. Approximation coefficients correspond to low frequency components of the signal. Next, the watermark generated by pseudorandom numbers is embedded into wavelet approximation coefficients of the segmented host audio signal depending on the binary value of the binary image. The reason for embedding the watermark in the low frequency components is that these components' energy is greater than that of high frequency components in such a way that the watermark is inaudible; therefore, it should not alter the audible content and should not be easy to remove. The proposed method uses a binary image to decide whether or not the watermark generated by pseudorandom numbers is embedded in the audio host signal. To evaluate the performance of the proposed audio watermarking method, subjective and objective quality tests including bit error rate (BER) and signal-to-noise ratio (SNR) are conducted. The tests' results show that the proposed method yields a high recovery rate after attacks by commonly used audio data manipulations such as low-pass filtering, requantization, resampling and MP3 compression.