watermarking using neural network and hiding the trained network ...

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1Reader,Department of Information Technology,MIT,Ujjain,India. 2Professor,Department of Electronics,MITS,Gwalior,India. E-mail: [email protected] ...

Journal of Theoretical and Applied Information Technology © 2005 - 2008 JATIT. All rights reserved. www.jatit.org


Er. Ashish Bansal , 2 Dr. Sarita Singh Bhadauria Reader,Department of Information Technology,MIT,Ujjain,India 2 Professor,Department of Electronics,MITS,Gwalior,India E-mail: [email protected] , [email protected]


ABSTRACT The major source of communication in present world is digital media. As it is quite easy to manipulate a digital media, it becomes essential to protect the digital information by legitimate means. Digital Watermarking has evolved as one of the latest technologies for digital media protection. Many techniques based on spatial and frequency domain have been developed in the recent past and are being used for effective watermarking. However, there is always a tradeoff between robustness and imperceptibility features of watermarking offered by these techniques. This paper offers a technique based on Backpropagation Neural Network to train a given cover image to produce a desired watermark image. At the end of the training, the entire trained neural network weights has been successfully hidden within the cover image itself. This makes it possible to supply only the cover image without any external weight files. By extraction techniques, the weights can be derived from the cover image and used to reconstruct the trained Neural Network again which in turn converts the cover image into desired watermark image. The technique for hiding the weights into the cover image has been designed in such a way that it does not produce visual deterioration of the original cover image. This method is extra secure as it leads to watermarking indirectly. Keywords :

Digital Watermarking, Information Hiding, Digital Media Protection, Neural Network

1. INTRODUCTION Digital watermarking should provide the qualities like imperceptibility, robustness, security of cover image.. A large number of techniques have been developed based on manipulating the bit plane of Least Significant Bit (LSB)[1], linear addition of watermark to cover image[1], using mid band coefficients of DCT transformed blocks to hide watermark[2], maximizing strength of watermark using Discrete Wavelet Transform(DWT) techniques[3], Using radial basis function (RBF)neural network to achieve maximum strength watermark[4], transforming color space of cover image and embedding watermark into saturation channel [5],Embedding watermark in the DC components of transformed blocks[6] etc. Principles of neurocomputing, and their usage in science and technology is well explained in [7] . Cox et al. [8] pointed that, in order for a watermark to be robust to attack, it must be placed in perceptually significant areas of the image.

Schyndel et al. [9] generated a watermark using a m-sequence generator. Bas et al . [9] introduced a watermarking scheme using fractal codes. Bartolini et al. [10] utilized the properties of human visual system and generated watermark from DCT coefficients. Kundur and Hatzinakos [11] embedded the watermark in the wavelet domain where the strength of watermark was decided by the contrast sensitivity of the original image. Delaigle et el. [12] generated binary m-sequences and then modulated on a random carrier. A method for casting digital watermarks on images and analyzing its effectiveness was given by I.Pitas[13] and immunity to subsampling was examined. Cox and Kilan [14] presented a secure algorithm for watermarking images using spread-spectrum techniques. Craver and Memon [15] proposed digital watermarks to resolve the copyright ownership. However, these techniques suffer from the problems of unsatisfactory value of imperceptibility and robustness to various attacks as discussed in these papers. These techniques also


Journal of Theoretical and Applied Information Technology © 2005 - 2008 JATIT. All rights reserved. www.jatit.org

have the problems related to security. The use of Neural Network for successful watermarking was effectively done in [16] , where Full Counterpropagation Network (FCNN) was used to insert the watermark into synapses of FCNN rather than the cover image. Chun –YuChang [16] proposed a wonderful technique of embedding the watermarks into synapses of FCNN rather than cover image. This helped to increase robustness and reduce imperceptibility problems to a great extent. This paper has described the use of a Backpropagation Neural Network to hide the cover image. After the neural network is trained, the trained network weights have been inserted into the cover image itself by using a special technique which preserves the visual quality of the original cover image. Section 2 discusses the approach for using Backpropagation with the help of broad outline of the technique of embedding and extraction. Section 3 provides the detailed algorithm for embedding and extraction . Section IV gives experimental results . Conclusion is given in Section 4 followed by references. 2. APPROACH BACKPROPAGATION WITH COVER IMAGE




cover image by using a special techniquedescribed in algorithm given in section III and the trained neural network is reconstructed. 2) The watermarked image is supplied at the input layer neurons and the final output watermark image is produced at the output layer. 3) The output watermark image is correlated with the target output watermark to determined PSNR of the obtained watermark image. 3. ALGORITHM The following conventions apply to the embedding algorithm as well as extraction algorithms given below. 1) M= rand(m,c) generates a random matrix M containing m rows and n columns . 2) M =zeros(m,c) generates a matrix of m rows and c columns containing all zeros. M(i,j) = 0 for 1

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