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Proceedings Paper

Parallel blind semi-fragil color image watermarking based on fast discrete cosine transform
Author(s): Alexis Jiménez-Calzadilla; Volodymyr I. Ponomaryov; Rogelio Reyes-Reyes; Clara Cruz-Ramos
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Paper Abstract

In this paper, a completely blind semi-fragile color image watermarking method for copyright protection is proposed. The embedding algorithm is performed by swapping three pairs of mid-frequency coefficients in each 8 by 8 Discrete Cosine Transform (DCT) non-overlapped block of the fast-discrete cosine transform algorithm that utilizes the eigenvectors of a symmetric “second difference” matrix in order to achieve higher computation performance. The simplicity of the embedding, extraction and DCT computation methods, offers a significant advantage in shorter processing time. Experimental results demonstrate the watermark imperceptibility with high PSNR and SSIM values of the watermarked image (of 45 dB and 0.948, respectively); additionally, the 2D binary watermark embedded such as company trademarks or owner’s logotype can be extracted with completely blind extraction process, i.e. without the original carrier image, original watermark nor any other information derivative of them are required. Average normalized correlation of the extracted binary watermark is of 0.91 even if the watermarked image was attacked by scaling, noise addition or JPEG compression. The simulation results of the parallel implementation in multicore CPU of the proposed method have shown more efficient and effective in real-time implementation of image watermarking than commonly used DCT techniques.

Paper Details

Date Published: 14 May 2019
PDF: 14 pages
Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960Q (14 May 2019); doi: 10.1117/12.2518477
Show Author Affiliations
Alexis Jiménez-Calzadilla, Instituto Politécnico Nacional (Mexico)
Volodymyr I. Ponomaryov, Instituto Politécnico Nacional (Mexico)
Rogelio Reyes-Reyes, Instituto Politécnico Nacional (Mexico)
Clara Cruz-Ramos, Instituto Politécnico Nacional (Mexico)

Published in SPIE Proceedings Vol. 10996:
Real-Time Image Processing and Deep Learning 2019
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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