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Proceedings Paper

Dynamic digital watermark technique based on neural network
Author(s): Tao Gu; Xu Li
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Paper Abstract

An algorithm of dynamic watermark based on neural network is presented which is more robust against attack of false authentication and watermark-tampered operations contrasting with one watermark embedded method. (1) Five binary images used as watermarks are coded into a binary array. The total number of 0s and 1s is 5*N, every 0 or 1 is enlarged fivefold by information-enlarged technique. N is the original total number of the watermarks' binary bits. (2) Choose the seed image pixel px,y and its 3×3 vicinities pixel p x-1,y-1,px-1,y,px-1,y+1,px,y-1,px,y+1,px+1,y-1,px+1,y,px+1,y+1 as one sample space. The px,y is used as the neural network target and the other eight pixel values are used as neural network inputs. (3) To make the neural network learn the sample space, 5*N pixel values and their closely relevant pixel values are randomly chosen with a password from a color BMP format image and used to train the neural network.(4) A four-layer neural network is constructed to describe the nonlinear mapped relationship between inputs and outputs. (5) One bit from the array is embedded by adjusting the polarity between a chosen pixel value and the output value of the model. (6) One randomizer generates a number to ascertain the counts of watermarks for retrieving. The randomly ascertained watermarks can be retrieved by using the restored neural network outputs value, the corresponding image pixels value, and the restore function without knowing the original image and watermarks (The restored coded-watermarkbit=1, if ox,y(restored)>px,y(reconstructed, else coded-watermarkbit =0). The retrieved watermarks are different when extracting each time. The proposed technique can offer more watermarking proofs than one watermark embedded algorithm. Experimental results show that the proposed technique is very robust against some image processing operations and JPEG lossy compression. Therefore, the algorithm can be used to protect the copyright of one important image.

Paper Details

Date Published: 19 April 2008
PDF: 7 pages
Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 69790I (19 April 2008); doi: 10.1117/12.783953
Show Author Affiliations
Tao Gu, North China Institute of Science and Technology (China)
Xu Li, North China Institute of Science and Technology (China)

Published in SPIE Proceedings Vol. 6979:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI
Harold H. Szu; F. Jack Agee, Editor(s)

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