Neural network based watermark embedding and identification

In previous research, we have shown the ability of neural networks to improve the performance of the watermark system to identify the watermark under different attacks. On the other hand, in this work we apply neural networks to embed the watermark in the discrete wavelet transform (DWT) domain. We then use features based on principal component analysis (PCA) to blindly identify the watermark. PCA reduces the dimensionality as well as the redundancies of the data. Neural networks classifiers are implemented to determine whether the watermark is present. Different features are used to test the performance of the method. The efficacy of the technique is then compared to previous techniques such as the gray level co-occurrence matrix (GLCM) based or the LMS enhanced watermark identification. The comparative results from the previously used methods are presented in this paper.