Audio watermark: Difference between revisions
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One of the most secure techniques of audio [[Digital watermarking|watermarking]] is spread spectrum audio watermarking (SSW). It hides information by spreading their spectrum which is called watermark and adds it to a host signal as a watermarked signal. 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 watermarked signal for detecting hidden information. This method is attributed high security features, since any unauthorized user who does not access this information cannot detect any hidden information. Detection of the PN sequence is the key factor for detection of hidden information from SSW. |
One of the most secure techniques of audio [[Digital watermarking|watermarking]] is spread spectrum audio watermarking (SSW). It hides information by spreading their spectrum which is called watermark and adds it to a host signal as a watermarked signal. 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 watermarked signal for detecting hidden information. This method is attributed high security features, since any unauthorized user who does not access this information cannot detect any hidden information. Detection of the PN sequence is the key factor for detection of hidden information from SSW. |
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Although PN sequence detection is possible by using heuristic approaches such as evolutionary algorithms, due to the high computational cost of this task, such heuristic tends to become too expensive (computationally speaking), which can turn it impractical. Much of the computational complexity involved in the use of [[evolutionary algorithm|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 fitness granulation approach called '''Adaptive Fuzzy Fitness Granulation |
Although PN sequence detection is possible by using heuristic approaches such as evolutionary algorithms, due to the high computational cost of this task, such heuristic tends to become too expensive (computationally speaking), which can turn it impractical. Much of the computational complexity involved in the use of [[evolutionary algorithm|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 fitness granulation approach called '''[http://www.davarynejad.com/Mohsen/index.php?n=Main.AdaptiveFuzzyFitnessGranulation Adaptive Fuzzy Fitness Granulation](AFFG)''', the expensive fitness evaluation step is replaced by an approximate model. |
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== References == |
== References == |
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Revision as of 10:04, 11 May 2009
One of the most secure techniques of audio watermarking is spread spectrum audio watermarking (SSW). It hides information by spreading their spectrum which is called watermark and adds it to a host signal as a watermarked signal. 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 watermarked signal for detecting hidden information. This method is attributed high security features, since any unauthorized user who does not access this information cannot detect any hidden information. Detection of the PN sequence is the key factor for detection of hidden information from SSW.
Although PN sequence detection is possible by using heuristic approaches such as evolutionary algorithms, due to the high computational cost of this task, such heuristic tends to become too expensive (computationally speaking), which can turn 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 fitness granulation approach called Adaptive Fuzzy Fitness Granulation(AFFG), the expensive fitness evaluation step is replaced by an approximate model.
References
- M. Davarynejad etc al., Detecting Hidden Information from Watermarked Signal using Granulation Based Fitness Approximation, World Conference on Soft Computing in Industrial Applications, 2008.