Data Hiding Method with Principal Component Analysis and Image Coordinate Conversion

Abstract

Data hiding method with Principal Component Analysis (PCA) and image coordinate conversion as a preprocessing of wavelet Multi Resolution Analysis (MRA) is proposed. The method introduced in this paper, based on the characteristics of the original multispectral image, allows recovering the secret data.Through experiments, it is found that the proposed method is superior to the conventional data hiding method without any preprocessing. The method introduced in this paper allows only I who knows the characteristics of the original multispectral image to recover the secret data, i.e., when the information of the original image needs to be protected. Moreover, in the introduced method, the information of the secret data is protected by the existence of the eigenvector and the oblique coordinate transformation, that is, the secret data is restored if at least the information of the true original image is not known. The principal component transformation coefficient differs for each original image and is composed of the eigenvectors of the original image.

Wavelet Multi Resolution Analysis based Data Hiding with Scanned Secrete Images

“…Following are the related research works: Data hiding method replacing LSB of hidden portion for secrete image with Run-Length coded image is proposed [15]. Meanwhile, Data hiding method with Principal Component Analysis: PCA and image coordinate conversion is proposed for improvement of invisibility of the secret key image [16].…”