Hiding & Retrieval of Encrypted Data in an Image Using XOR Based on RBF and GRNN Networks and Comparison between their Performance

Abstract

In this paper an image is hidden in another image by applying logical XOR operation to the two least significant bit of each byte of the cover and two bits of the secret message to produce the stego-cover image which is used as an input with the cover to Radial Basis Function Network (RBFN) or General Regression Neural Network (GRNN) to produce the weights. Cover is delivered once to the recipient who can use it for unlimited number of messages. The weights are delivered to the recipient for each hidden message as a key. The recipient uses the cover with the weights to unhide the message, so that this method includes two levels of security. The first one is hiding the message in the cover to produce stego-cover image. The second one is ciphering the embedded image using RBF Neural Network or GRNN. The stego-cover is considered as a target and the input to the neural network is the cover image. Then the weights, which represent the encrypted information are reconstructed by training the neural network. The recipient can use RBF Network or GRNN to unhide the message by having the stego-cover image then the message. In this paper the performance of RBF Neural Network and GRNN are compared. Noted that the GRNN is better than RBF for this work. Matlab R2008a was used in this paper.