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Damageless Information Hiding using Neural Network on YCbCr Domain


Kensuke Naoe, Yoshiyasu Takefuji


Vol. 8  No. 9  pp. 81-86


In this paper, we propose a new information hiding technique without embedding any information into the target content by using neural network trained on frequency domain especially on YCbCr domain. Proposed method can detect a hidden bit codes from the content by processing the selected feature subblocks into the trained neural network. Hidden codes are retrieved from the neural network only with the proper extraction key provided. The extraction keys, in proposed method, are the coordinates of the selected feature subblocks and the network weights generated by supervised learning of neural network. The supervised learning uses the coefficients of the selected feature subblocks as set of input values and the hidden bit patterns are used as teacher signal values of neural network. With our proposed method, we are able to introduce a information hiding scheme with no damage to the target content. There are many digital watermarking and steganographic algorithms been proposed, but there are difficulties to use one algorithm together with another because each other obstruct the embedded information and causing one to destroy another. Because our proposed method does not damage the target content, it has an ability to collaborate with another algorithm to strengthen the security of information hiding method.


Digital Watermarking, Steganography, Information Hiding, Digital Rights Management, Neural Network