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Performance comparison of optimization techniques on robust digital image watermarking against attacks

Publication Type : Journal Article

Publisher : Taylor and Francis

Source : Applied Artificial Intelligence – Taylor and Francis, Volume 26, Issue 7, p.615-644 (2012)

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Campus : Bengaluru

School : School of Engineering

Department : Electrical and Electronics

Year : 2012

Abstract : Increasing illegal exploitation and imitation of digital images in the field of image processing has led to the urgent development of copyright protection methods. Digital watermarking has proved to be the most effectivemethod for protecting illegal authentication of data. In this article, we propose a hybrid digital-image watermarking scheme based on computational intelligence paradigms such as a genetic algorithm (GA) and particle swarm optimization (PSO). The watermark image is embedded into the host image using discrete wavelet transform (DWT). During the extraction process, GA, PSO, and the hybrid combination of GA and PSO are applied to improve the robustness and fidelity of the watermarked image by evaluating the fitness function. The perceptual transparency and the robustness of both the watermarked and the extracted images is evaluated by applying filtering attacks, additive noise, rotation, scaling, and JPEG compression attacks to the watermarked image. From the simulation results, the performance of the hybrid particle swarm optimization technique is proved best, based on the computed robustness and transparency measures, as well as the evaluated parameters of elapsed time, computation time, and fitness value. The performance of the proposed scheme was evaluated with a set of 50 textures images taken from online resources of Tampere University of Technology, Finland, and the entire algorithm for different stages was simulated using MATLAB R2008b.

Cite this Research Publication : P. Surekha and Sumathi, S., “Performance comparison of optimization techniques on robust digital image watermarking against attacks”, Applied Artificial Intelligence – Taylor and Francis, vol. 26, no. 7, pp. 615-644, 2012.

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