Improving LSB Algorithm Using Filtering and Matching

ABSTRACT: In the age of computers and communications, different techniques are developed in order to protect the information against illegal accesses and attacks. Steganography is one of these techniques which embeds secret information in a media such as image, sound, video, and etc. in a way that it is not detectable by others. Focusing on the image, Least Significant Bit (LSB) method is one of basic methods of image steganography in which the least significant bit of pixel colors are replaced with the bits of secret message simply. Selected Least Significant Bits (SLSB) method improves simple LSB method by embedding secret message bits to one of red, green, or blue color along filtering and matching approaches [1]. In the present thesis, a new filtering method which improves LSB method has been proposed. The filtering method selects a color or colors of pixel for embedding according to their most significant bits (MSB; the bits of color which do not participate in embedding) value. The colors whose most significant bits value are greater than a particular threshold will participate in embedding and the other ones will be skipped. This mechanism makes the recovery of hidden message possible. More, a matching technique ensures the most possible closeness of new generated color in the embedding process to its original color. In other words, first bit next to the least significant bits would change in favor of closing new generated color to its origin and lead to less visibility of stego image degrading. Quality of embedding is measured by some statistical metrics namely AAD, MSE, LP-Norm, LMSE, SNR, PSNR, NCC. Applying the same cover images and secret messages to forenamed methods, the new proposed method offers up to %50 better results in some metrics in comparison to LSB and SLSB methods. Keywords: LSB (Least Significant Bit), Filtering, Matching, Statistical Metrics. ……………………………………………………………………………………………………………………………………………………………………………………………………………………