Abstract In the study of content authentication and tamper detection of digital text documents, there are very limited techniques available for content authentication of text documents using digital watermarking techniques. A novel intelligent text zero-watermarking approach based on probabilistic patterns is proposed in this paper for content authentication and tamper detection of text documents. In the proposed approach, Markov model of... In the study of content authentication and tamper detection of digital text documents, there are very limited techniques available for content authentication of text documents using digital watermarking techniques. A novel intelligent text zero-watermarking approach based on probabilistic patterns is proposed in this paper for content authentication and tamper detection of text documents. In the proposed approach, Markov model of order TWO and letter-based was constructed for text analysis and utilizes the interrelationship between contents of given text to generate the watermark. However, we can extract this watermark later using extraction and detection algorithm to identify the status of text document such as authentic, or tampered. The proposed approach is implemented using PHP programming language. Furthermore, the effectiveness and feasibility of the proposed approach is proved and compared with other recent approaches with experiments using six datasets of varying lengths and volumes of attacks. Results show that the proposed approach is always detects tampering attacks occurred randomly on text even when the tampering volume is low or high. Comparative results with the recent approach show that the proposed approach provides added value under random insertion and deletion attacks, and worst under reorder attacks Read More Read Less

Abstract This paper presents a high-level parametric approach for synthesis of novel human animation sequences from short clips data. The parametric approach generates motions according to user-defined high-level motion parameters, such as speed, slope and direction, while maintaining the quality and realism of captured motions, and preserving the motion constraints. The approach allows simultaneous control over multiple motion parameters. This is... This paper presents a high-level parametric approach for synthesis of novel human animation sequences from short clips data. The parametric approach generates motions according to user-defined high-level motion parameters, such as speed, slope and direction, while maintaining the quality and realism of captured motions, and preserving the motion constraints. The approach allows simultaneous control over multiple motion parameters. This is also done with efficient computations. Animation sequences are synthesized by parametric blending, according to high-level parameters. The mapping between high-level parameters and low-level blending coefficients is automatically precomputed, enabling efficient motion synthesis with high-level parametric control. This allows realistic synthesis of novel motions intuitively and without expert animation skills. Hence, the presented high-level parametric approach allows both novice and expert users/animators to define their desired motion using intuitive parameters. This improves the learning curve for novice users and productivity for expert users. Another novel contribution is the introduction of quantitative, in addition to the qualitative, evaluation of the results which shows that synthesized animations are perceptually and quantitatively indistinguishable from captured animations. Example applications demonstrating the proposed approach are also presented. Read More Read Less

Abstract Human face recognition has become an active area of research over the last decade. The major problem of face recognition is the classification. In this paper, a new face recognition algorithm based on fusion of 2DPCA and Gabor features with SVM classifier is presented. The method first extracts features by employing Gabor wavelets followed by a face recognition algorithm 2DPCA... Human face recognition has become an active area of research over the last decade. The major problem of face recognition is the classification. In this paper, a new face recognition algorithm based on fusion of 2DPCA and Gabor features with SVM classifier is presented. The method first extracts features by employing Gabor wavelets followed by a face recognition algorithm 2DPCA and the SVM method is applied to classify image faces. The performance of the proposed algorithm is tested on the public and largely used databases of FRGCv2 face and ORL databases. Experimental results on databases show that the use of SVM can achieve promising results. Read More Read Less