Combined Markov Model and Zero Watermarking Techniques to Enhance Content Authentication of English Text Documents
Mokhtar M. Ghilan, Fadl M. Ba-Alwi, Fahd N. Al-Wesabi
International Journal of Computational Linguistics Research, Vol. 5, No. 1, 2014.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v5n1/3.pdf
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. In this paper, we have extended the proposed LNMZW3 algorithm presented in [27] for content authentication and tamper detection of English text documents and abbreviated as ADV-LNMZW3. In the enhanced algorithm , third order and letterbased of Markov model has uses in this paper as soft computing tools in order to English text analyse in order to find the inter-relationships and utilize theses features to generate and detect a watermark in order to identify the status of text document such as authentic, or tampered. One of the important enhancements has done on LNMZW3 algorithm and presented in this paper is study the performance average of our enhanced algorithm named as ADV-LNMZW3 against different sizes of datasets and compare them with two modern proposed algorithms named LNMZW1 and LNMZW2. The study of dataset size effect also has been examined in this paper against common volumes of insertion, deletion and reorder attacks. The enhanced algorithm (ADV-LNMZW3) was implemented using PHP Programming language with Net Beans IDE 7.0. Furthermore, the effectiveness and feasibility of our ADV-LNMZW3 algorithm has proved and compared with other recent algorithms with experiments using five datasets of varying lengths and different volumes of attacks. The experiment showed that the ADV-LNMZW3 algorithm had better performance and robustness, and it is more secure than other algorithms especially in case of insertion and deletion attacks.The effect of document size on watermark robustness was examined. The results showed that the watermark robustness is enhanced with small and medium documents size. The results showed that our ADV-LNMZW3 algorithm was applicable for all sizes of text document, and it is recommended for tampering detection against small, and medium sizes of text documents. However, the LNMZW1 was found to be applicable under large size of text documents.