An audio watermark is a unique electronic identifier embedded in an audio signal, typically used to identify ownership of copyright. It is similar to a watermark on a photograph.
Digital watermarking is the process of embedding information into a signal (e.g. audio, video or pictures) in a way that is difficult to remove. If the signal is copied, then the information is also carried in the copy. Watermarking has become increasingly important to enable copyright protection and ownership verification.
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Transcription
Spread spectrum
One technique for audio watermarking is spread spectrum audio watermarking (SSW). In SSW, a narrow-band signal is transmitted over a much larger bandwidth such that the signal energy presented in any signal frequency is undetectable. Thus the watermark is spread over many frequency bands so that the energy in one band is undetectable. An interesting feature of this watermarking technique is that destroying it requires noise of high amplitude to be added to all frequency bands.
Spreading spectrum is done by a pseudonoise (PN) sequence. In conventional SSW approaches, the receiver must know the PN sequence used at the transmitter as well as the location of the watermark in the watermarked signal for detecting hidden information.
Although PN sequence detection is possible by using heuristic approaches such as evolutionary algorithms, the high computational cost of this task can make it impractical. Much of the computational complexity involved in the use of evolutionary algorithms as an optimization tool is due to the fitness function evaluation that may either be very difficult to define or be computationally very expensive.
One of the recent proposed approaches—in fast recovering the PN sequence- is the use of fitness granulation as a promising "fitness approximation" scheme. With the use of the fitness granulation approach called "Adaptive Fuzzy Fitness Granulation (AFFG)",[1] the expensive fitness evaluation step is replaced by an approximate model. When evolutionary algorithms are used as a means to extract the hidden information, the process is called Evolutionary Hidden Information Detection, whether fitness approximation approaches are used as a tool to accelerate the process or not.
See also
References
- ^ Davarynejad, Mohsen. "Adaptive Fuzzy Fitness Granulation". behsys analytics. Archived from the original on 2021-12-06. Retrieved 2017-07-10.
- M. Davarynejad, S. Sedghi, M. Bahrepour, C.W. Ahn, M. Akbarzadeh, C. A. Coello Coello, "Detecting Hidden Information from Watermarked Signal using Granulation Based Fitness Approximation", Applications of Soft Computing: From Theory to Praxis, Springer, Series: Advances in Intelligent and Soft Computing, Volume 58/2009, ISBN 978-3-540-89618-0, pp. 463–472, 2009.
- M. Davarynejad, C.W. Ahn, J. Vrancken, J. van den Berg, C .A. Coello Coello, "Evolutionary hidden information detection by granulation-based fitness approximation Archived 2021-12-06 at the Wayback Machine", Applied Soft Computing, Vol. 10(3), pp. 719–729, 2010, doi:10.1016/j.asoc.2009.09.001.
This page was last edited on 13 October 2023, at 19:27