An Adaptive Image Steganographic Model Based on Minimum-Error LSB Replacement

Steganography is an ancient art of conveying message in a secret way that only the receiver knows the existence of message. Steganalysis is the art of detecting the message's existence and blockading the covert communication. The least-significant bit (LSB) insertion method, which uses fixed k LSBs in each pixel to embed secret message, is the most common and easy one to hide message in an image. However, it is easy to reveal a stego-image produced by the LSB insertion method. In this paper, we will first lead a bit-plane steganalysis on such stego-images. Then, an adaptive steganographic model will be proposed to take off the restriction of fixed embedding size in each pixel. The model will reduce the embedding error and provide higher embedding capacity. Moreover, to detect the message's existence will be very hard for those stegoimages produced using the proposed model.