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In this article, we propose a method of key generation scheme (Figure 1) for static visual digital watermarking (Figure 2) by using machine learning technology, neural network as its exemplary approach for machine learning method.
Figure 1. Key generation scheme in embedding procedure
Figure 2. Watermark extraction scheme in extraction procedure
The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key which is conditioned to generate watermark signal patterns only if proper visual features are presented to the classifier. In our proposed method, this classifier which is generated by the machine learning process is used as watermark extraction key.
The proposed method is to contribute to secure visual digital watermarking without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects. The proposed method has used neural network for its training approach not limited but open in its applications to other machine learning approaches including fuzzy, Bayesian network and others. In this article, the target content is a static visual data which are constructed with discrete data set and we have demonstrated the feasibility of solving this problem by using neural network model. We would enhance our method by using those other approaches, such as fuzzy for dynamic visual data like video stream data and Bayesian network for continuous data structures. This article is different from the previous work by Ando et al. (Ando, R. & Takefuji, Y., 2003) in terms of embedding size where this article does not embed any information to the target content and also implies that the machine learning algorithm is not limited only to the neural network model as proposed in our previous work (Naoe, K. & Takefuji, Y., 2008).