Steganographic techniques using modified least significant bit and modification reshape transposition methods

A message is a form of conveying information. Various ways are used to secure the information conveyed in the form of messages either in encrypted form or in the form of applying a password in the message. Messages can also be encrypted and embedded in other media such as images (steganography). This research aimed to insert a message into the form of an image by combining the Modified Least Significant Bit (MLSB) method in encrypting messages and reshape modification technique to determine at which position the message encryption will be embedded in the image. Tests were carried out to obtain the quality of the encryption process using the parameters of Fidelity, mean square error, peak signal to noise ratio, testing on file type, robustness, and comparison of message contents. The results of the tests showed that the files that can be used are files with the image file type in the lossless compression category, the rotation can be done at 90, 180, 270 without destroying the message in it, and changing the pixel in the image file will destroy the message insid

Effectiveness of LSB and MLSB information embedding for BMP images

The paper presents a study of efficiency of information embedding in BMP images by using codes perfect in a weighted Hamming metric (WHM) and standard Hamming codes. The weighted container model and modification of algorithm F5 is used, which allows to record information in multiple bit planes images (MLSB), taking into account the significance of each bit plane. In this paper the effectiveness of such weighted embedding approach is compared with the common matrix information embedding method that uses classic Hamming code (7,4,3). The effectiveness of information embedding is estimated by PSNR metric and by special Penalty function value. Also composition of RGB color channels in different order is attempted to define the best weighted container structure taking into account Human Vision System features