Audio scrambling technique based on cellular automata

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1306-7Scrambling is a process that has proved to be very effective in increasing the quality of data hiding, watermarking, and encryption applications. Cellular automata are used in diverse and numerous applications because of their ability to obtain complex global behavior from simple and localized rules. In this paper we apply cellular automata in the field of audio scrambling because of the potential it holds in breaking the correlation between audio samples effectively. We also analyze the effect of using different cellular automata types on audio scrambling and we test different cellular automata rules with different Lambda values. The scrambling degree is measured and the relation between the robustness and the scrambling degree obtained is studied. Experimental results show that the proposed technique is robust to data loss attack where 1/3 of the data is lost and that the algorithm can be applied to music and speech files of different sizes.This work is partially supported by the Spanish Ministry of Science and Innovation under coordinated research projects TIN2011-28260-C03-00 and TIN2011-28260-C03-02 and by the Comunidad Autónoma de Madrid under research project e-madrid S2009/TIC-165

Blind audio watermarking technique based on two dimensional cellular automata

In this paper we propose a new method of digital audio watermarking based on two dimensional cellular automata; the method increases the dimension of the audio and uses cellular automata in generating the key of watermark embedding. The watermarking method is blind, and does not require the original host audio or any of its features to extract the watermark; the watermark can be easily extracted using the right key. The experimental results show that the watermarks are imperceptible; and show a high similarity between the original and the watermarked audio. Cosine similarity and peak signal-to-noise ratio were used to measure the similarity between the original audio and the watermarked audio

Paper-Based Watermark Extraction with Image Processing

This thesis presents frameworks for the digitisation, localisation, extraction and graphical representation of paper-based watermark designs embedded in paper texture. There is a growing need for this among librarians and antiquarians to aid with identification, wider accessibility, and providing a further level of document imaging for preservation. The proposed approaches are designed to handle manuscripts with interference such as recto and verso writing, and defects such as non-uniform paper structure, physical damage, etc. A back-lighting scanning technique is used for capturing images of paper, followed by a selection of intelligent image processing operations, rather than alternatives such as radioactive techniques. This technique requires low cost equipment, and produces a fast and safe solution to capturing all details on paper, including watermarks, and laid and chain lines patterns. Two approaches are presented: the first takes a bottom-up approach and deploys image processing operations to enhance, filter, and extract the watermark, and convert it into a graphical representation. These operations determine a suitable configuration of parameters to allow optimal content processing, in addition to the detection and extraction of chain lines. The second approach uses a model of the back-lighting effect to locate a watermark in pages of archaic documents. It removes recto information, and highlights remaining ‘hidden’ data, and then presents a statistical approach to locate watermarks from a known lexicon. Work is further presented on reconstructing features of the paper mould by aggregating the success of the foregoing steps: this permits an analysis of ‘twin’ watermarks. Results are presented from comprehensively scanned eighteenth and nineteenth century manuscripts, including two unusual copies of the Quran, an Islamic Prayer, and various historical documents