Study Of The Phase-only Optical Anti-counterfeiting Mask Design And Optimization

The rapid development of information technology not only promotes social development and brings convenient to the life of people, the fraud cases is increasing, which uses the high-tech means to counterfeit famous brand trademark, important documents(ID card, driving license, etc), credit card, etc. Which have caused a huge loss to the nation, business and personal property. And these cases are more dangerous and secret, so it is difficult to solve. Thus make it very important to improve anti-counterfeiting ability for various certificates, meanwhile, continuously exploring better and more secure anti-counterfeiting technologies is always the most important research topic in the field of information security.Optical anti-counterfeiting technology to code and hide information has many advantages, which include multidimensional, strong robustness, large amount of information, difficult crack and etc. therefore it has very high value in academic and application that using optical and photoelectric method explores and develops the information security system. The main work of this paper is to study some problems which is about the random phase encoding and recovery phase in the phase-only optical anti-counterfeiting mask. In this research project, the anti-counterfeiting logo image and text is coded under the control of a particular algorithm based on the random phase encoding technology, and the random distribution of pure phase information was got. Then the transparent pure phase optical anti-counterfeiting mask was made through some processing technology.This paper mainly includes:(1) In the aspect of basic theory, briefly analyzed the theore-tical basis of the design and make of the pure phase optical anti-counterfeiting mask, and several common iterative phase recovery algorithm. Then the computer simulation was carried out. The advantages and disadvantages of these algorithms were showed clearly through the actual data. (2) Co-evolutionary genetic algorithm based on genetic algorithm was presented, and co-evolutionary genetic algorithm and GS algorithm combined algorithm was presented, which combined the advantages of co-evolutionary genetic algorithm and GS algorithm, namely the CGA-GS algorithm. Then the convergence speed of the algorithm, the quality of image restoration and the precision of GS algorithm calculation were effectively improved. (3) A improved algorithm was presented based on simulated annealing algorithm, and a combined algorithm based on the improved simulated annealing algorithm and GS algorithm was proposed which combined the advantages of improved simulated annealing algorithm and GS algorithm, this algorithm made full use of the global searching ability of SA algorithm, and effectively solved the problem that the GS algorithm was easy to fall into local optimal solution. Effectively improved the convergence speed of the algorithm, the quality of image restoration, and the precision of GS algorithm calculation.