A noise-type Gaussian sequence is most commonly used as a watermark to claim ownership of media data. However, only a 1 bit information payload is carried in this type of watermark. For a logo-type watermark, the situation is better because it is visually recognizable and more information can be carried. However, since the sizes and shapes of logos for different organizations are different, the flexibility of use of a logo-type watermark will certainly be degraded. We design a more flexible type of watermark, i.e., a message. Since a message is composed of a finite number of ASCII-type characters, it is by nature vulnerable to attacks. Therefore, we propose to choose a set of nonlinear Hadamard codes that has the maximum Hamming distance between any two constituent codes to replace the original ASCII-type inputs. This design will make our system much more fault-tolerant in comparison with ASCII-code based systems under direct attack. To recover an attacked Hadamard code, we use a trained backpropagation neural network to perform inexact matching. Experimental results demonstrate that our message-based cocktail watermarking system is superb in terms of robustness and flexibility.
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