Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: The rapid development of communication technology due to the global spread of the Internet and the digital information revolution has given rise to a huge increase in the use and transmission of multimedia information (images, audio, and video). As a result, information security during storage and transmission has become a critical issue. For example, images are widely used in industrial processes. These images could contain private information, so they must be protected.Digital image scrambling, often used for image encryption and data hiding, reorders and changes the position of image pixels to break the relationship between adjacent pixels. 1 These methods include Advanced Encryption Standard, 2 Twice Interval Division, 3 Cat Chaotic Mapping, 4 Magic Cube, 5 and Arnold Transformation. 6 We propose a new scrambling method based on a 2D cellular automaton (CA).The ability to obtain complex global behavior from simple local rules makes CA an interesting platform for digital image scrambling. The most widely known example, the Game of Life (GL), is a 2D CA that produces large amounts of patterned data. The GL (which was designed by John Conway) can scramble the digital image by providing the complex behavior that would produce the most useful operationsIn this work, we analyze the GL's complex characteristics using digital image scrambling to decide whether the degree of scrambling is influenced by different GL configurations (such as the number of generations and boundary conditions). We also design various sets of 2D CA rules, variations on the GL rules, with different Lambda parameters around the critical value of Lambda. Our resulting model is simple and robust, and our tests show that the scrambling effects are good. Cellular AutomataCA are widely used in applications such as art (generated images and music), random number generation, pattern recognition, routing algorithms, and games. The application of CA in the area of digital image processing includes image enhancement, compression, encryption, and watermarking. 8 CA are dynamic, complex space and time discrete systems originally proposed by Stanislaw Ulam and John von Neumann in the 1940s as formal models for self-reproducing organisms. 7 They consist of a certain number of identical cells, each of which can take a finite number of states. The cells are distributed in space in a rectangular grid in one or more dimensions. At every time step, all the cells update their states synchronously by applying rules (transition function), which take as input the state of the cell under consideration and the states of its neighboring cells. The various CA models differ in the number of dimensions, the number of possible states, the neighborhood relationship, and the state update rules.In spite of their simple construction, CA can produce complex behavior and generate useful operations. Stephen Wolfram classified 1D CA into four broad categories: clas...
Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract Scrambling 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 achieving a high scrambling degree. 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 relation between the robustness and the scrambling degree is also studied. Experimental results show that the proposed technique is robust to data loss attack and can be applied to different applications based on the scrambling degree required.
Lindenmayer Grammars have been applied frequently to represent fractal curves. In this work, the ideas behind Grammar Evolution are used to automatically generate and evolve Lindenmayer Grammars that represent fractal curves with a fractal dimension that approximates a pre-defined required value. For many dimensions, this is a non trivial task to be performed manually. The procedure we are proposing here closely parallels biological evolution, because it acts through three different levels: a genotype (a vector of integers), a protein-like intermediate level (the Lindenmayer Grammar) and a phenotype (the fractal curve). Variation acts at the genotype level, while selection is performed at the phenotype level (by comparing the dimensions of the fractal curves to the desired value).
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