An audio watermark is a kind of digital watermark —a marker embedded in an audio signal, typically to identify ownership of copyright for that audio. 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. A signal may carry several different watermarks at the same time. Watermarking has become increasingly important to enable copyright protection and ownership verification.
One of the most secure techniques of audio watermarking is spread spectrum audio watermarking (SSW). Spread Spectrum is a general technique for embedding watermarks that can be implemented in any transform domain or in the time domain. 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 bins so that the energy in one bin is undetectable. An interesting feature of this watermarking technique is that destroying it requires noise of high amplitude to be added to all frequency bins. This type of watermarking is robust since to be confident of eliminating a watermark, the attack must attack all possible frequency bins with modifications of considerable strength. This will create visible defects in the data.
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 is a high security feature, since any unauthorized user who does not have 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, 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 fitness granulation approach called Adaptive Fuzzy Fitness Granulation(AFFG), the expensive fitness evaluation step is replaced by an approximate model. When evolutionary algorithms are used as a means to extract the hidden information, it is called Evolutionary Hidden Information Detection, whether fitness approximation approaches (as a tool to accelerate the process) are used or not.