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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.17 n.3 México Jul./Sep. 2013

 

Artículos

 

A Parallel PSO Algorithm for a Watermarking Application on a GPU

 

Algoritmo paralelo PSO para una aplicación de marcas de agua en un GPU

 

Edgar García Cano1 and Katya Rodríguez2

 

1 Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, Mexico. eegkno@gmail.com

2 Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico. katya@uxdea4.iimas.unam.mx

 

Article received on 20/01/2013;
accepted on 11/08/2013.

 

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.

Keywords: Parallel particle swarm optimization, watermarking, CUDA, image security.

 

Resumen

En este artículo se presenta una investigación de la usabilidad, ventajas y desventajas de usar Compute Unified Device Architecture (CUDA) implementando un algoritmo basado en poblaciones, Optimización por Cúmulo de Partículas (PSO) [5]. Para probar el rendimiento del algoritmo propuesto, se realizó la implementación de una aplicación de marcas de agua ocultas. El PSO es usado para optimizar las posiciones donde la marca de agua debe ser insertada. Esta aplicación usa el algoritmo de inserción/extracción propuesto por Shieh et al. [1]. El algoritmo completo fue implementado para las arquitecturas secuenciales y CUDA. La función de optimización —usada en el algoritmo de optimización— es la unión de dos objetivos: fidelidad y robustez. La medición de la fidelidad y robustez es procesada usando el Error Cuadrático Medio (MSE) y la Correlación de Normalización (NC) respectivamente; estas funciones son evaluadas usando dominancia de Pareto.

Palabras clave: Optimización por cúmulo de partículas en paralelo, marcas de agua, CUDA, seguridad en imágenes.

 

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Acknowledgements

The first author, postgraduate student in Computer Science and Engineering at the National University of Mexico, expresses his gratitude to the support received from CONACYT (scholarship number 37617). The authors also express their gratitude to the support received from PAPIIT (project number 109011).

 

References

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