Research On The Digital Watermarking Technique Based On The Support Vector Machine

With the rapid development of computer, network and multimedia techniques, the multimedia security becomes a quite important and challenging research topic. Digital watermarking technique is a new method for digital content protection such as copyright protection, content authentication, transaction tracking, copy control, broadcasting monitoring and secret communication, and becomes an active research area in the field of media security.A kind of new machine learning method, namely support vector machine (SVM), is hopefully introduced to improve the watermark performance as better as possible. Meanwhile, it can also extend the new application of SVM in the filed of image processing and information security. Some potential schemes are analyzed in detail about using SVM to enhance the performance of image watermarking after analyzing the theory of SVM and the deficiency of current image watermarking, parts of schemes are studied deeply in this dissertation.Optimal SVM parameters selection used in the field of digital watermarking is studied, and a mutative scale chaos optimization algorithm is proposed based on the chaos variables which considered the selection problem of SVM parameters as a compound optimization problem by searching optimal objective function. In order to improve the efficiency, searching range of variables is shrunk continually during optimization according to the temporary optimal result. Based on the above result, the influence of support vector regression (SVR) parameters on the image watermarking performance is analyzed, and the ideal value range of SVR parameters is given respectively for different images.A blind spatial domain watermarking scheme based on support vector regression is proposed which considering the correlation among neighboring pixels in an image. It uses SVR to learn the relation between the central pixel and its neighboring pixels by choosing limited training samples and suitable SVR learning parameters, then, a bit of the watermark is embedded or extracted based on this relation model. The presented scheme can extract the watermark without the help of the original cover image and watermark image. Experimental results show the effectiveness of the proposed scheme.An adaptive spatial domain image watermarking algorithm based on fuzzy multi- classification support vector machine (FMSVC) is proposed. According to the very close similarity between SVM and human visual system (HVS) in self-learning, generalization and non-linear approximation, SVM is used to classify the pixels based on the characteristic of entropy, luminance, contrast and texture. Sequentially, the watermark embedding locations and strength are identified adaptively. The notable characteristic of the scheme is that, a kind of fuzzy multi-classification method based on SVM is presented which adopts fuzzy c-mean clustering algorithm, a kind of un-supervisory machine learning method, to construct training samples for FMSVC. Experimental results show the effectiveness of the proposed scheme.Two kinds of watermarking schemes, namely robust and semi-fragile watermarking, are proposed based on the coefficient direction-tree structure in the discrete wavelet transform domain. First, the concept of wavelet coefficient direction-tree is defined, then the inherent relation between root node and its offspring in the direction-tree is discovered and modeled using SVM, finally the above two watermarking scheme are designed based on the direction-tree model. The proposed robust watermarking scheme combines the spatial domain and transform domain, and selects the watermark embedding locations adaptively from the spatial domain by using fuzzy clustering method. The proposed semi-fragile watermarking scheme chooses the watermark embedding locations randomly with the secret key. So, the watermark is hardly detected without the knowledge of SVM parameters and secret key. Specially, since SVM binds the relation among wavelet coefficients, any modification will affect the recover of watermark bit. In this scheme, a tempering information matrix (TIM) and a local tempering appraising function (TAFL) is defined respectively. TAFL is calculated with a sliding widow which scans the TIM operated by medium filtering, and the maximal TAFL, namely TAFM is used to distinguish the common image operations (such as JPEG compression, filtering, noise) or the malicious tempering operations. Experimental results show the effectiveness of two proposed scheme.