Study On Digital Image Watermarking Algorithms Based On Artificial Neural Networks

The thesis focuses on the applications of neural networks in digital image watermarking algorithms. The basic concepts, algorithmic frames and application areas are described; the basic theory of neural network and the present situation of its applications in digital watermarking are described; some new schemes about image scramble and image watermarking are presented. The main innovative points of this thesis are as follows: (1) A new numerical index to score the image scrambling extent is presented. The index takes the two factors of pixel shifting distance and vision effects into account. Experimental results show that the proposed numerical index suits the intuitionistic vision effects perfectly. (2) A new image scrambling method based on BP neural networks is presented. Experimental results show that this method has better capabilities than Arnold transform, Fibonacci transform and affine transform in resisting noise adding, JPEG compression and image cropping, it can be used to the pre-processing of digital watermarking and image encryption. (3) A new image watermarking algorithm in spatial domain using RBF and Hopfield network is presented. The algorithm has the advantage of transparenting, robustness and no original image is needed in extraction process. Experimental results show that the algorithm has stronger robustness in noise adding, cropping and JPEG compression, and even when the strength of the salt-and-peper noise is 0.7 the watermark information can be extracted. (4) A new digital image watermark algorithm based on the association memory ability of neural network is proposed. This algorithm can improve the extracted watermarking image quality evidently so that strength the robustness of the algorithm. Experimental results show that this method can resist noise adding, JPEG compression, image cropping and filtering.