A Parallel PSO Algorithm for a Watermarking Application on a GPU

Abstract. In this paper, a research about the usability, advantages and disadvantages of using Compute Unified Device Architecture (CUDA) is presented, implementing an algorithm based on populations called Particle Swarm Optimization (PSO) [5]. In order to test the performance of the proposed algori...

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Detalhes bibliográficos
Autores: García Cano, Edgar, Rodríguez, Katya
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2013
País:México
Recursos:Instituto Politécnico Nacional
Repositório:Repositorio Digital del IPN
OAI Identifier:oai:www.repositoriodigital.ipn.mx:123456789/17230
Acesso em linha:http://www.repositoriodigital.ipn.mx/handle/123456789/17230
Access Level:Acceso aberto
Palavra-chave:Keywords. Parallel particle swarm optimization, watermarking, CUDA, image security.
Descrição
Resumo:Abstract. In this paper, a research about the usability, advantages and disadvantages of using Compute Unified Device Architecture (CUDA) is presented, implementing an algorithm based on populations called Particle Swarm Optimization (PSO) [5]. In order to test the performance of the proposed algorithm, a hide watermark image application is put into practice. The PSO is used to optimize the positions where a watermark has to be inserted. This application uses the insertion/extraction algorithm proposed by Shieh et al. [1]. This algorithm was implemented for both sequential and CUDA architectures. The fitness function—used in the optimization algorithm—has two objectives: fidelity and robustness. The measurement of fidelity and robustness is computed using Mean Squared Error (MSE) and Normalized Correlation (NC), respectively; these functions are evaluated using Pareto dominance.

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