Decreasing Change Impact Using Smart LSB Pixel Mapping and Data Rearrangement

The paper introduces a new optimization strategy in LSB steganography that reduces the image degradation rate in the steganographic carrier. We propose an LSB matching steganographic algorithm based on the principles of genetic algorithms, that aims to reuse the binary image color values in a controlled way so that instead of focusing to change the least significant portion of the color representation (LSBs), we remap the secret data in a manner that reduces the color information loss up to a negligible level. The algorithm improves the statistical analysis immunity of the steganographic image and at the same time offers higher PSNR (an average gain of 2,4 dB) than most of the LSB matching algorithms used in our experiments. Because of the flexibility of this approach, our method represents not only a stand-alone steganographic algorithm, but also an extension to other similar algorithms.