In this paper, a robust image watermarking technique using support vector regression (SVR) and particle swarm optimization is introduced to protect intellectual property rights of the gray images in discrete cosine transform domain against a variety of desynchronization attacks. After the division of the original image to 8 × 8 non-overlapping blocks, frequency coefficients of each block are found using discrete cosine transform. Positions of the inputs and output, among the low frequency coefficients which have the significant characteristics of the image, which are used to train SVR are obtained by using particle swarm optimization technique. After SVR is trained using the obtained positions of the inputs and output, watermark embedding and extracting processes are implemented using the trained SVR. Experiments implemented using the optimized coefficients selected among low frequency coefficients show that our watermarking technique has better watermark extracting success after the desynchronization attacks.
Key words: Robust image watermarking, support vector regression, particle swarm optimization.
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