Steganalysis for LSB Matching in Images with High-frequency Noise

Considerable progress has been made in the detection of steganographic algorithms based on replacement of the least significant bit (LSB) plane. However, if LSB matching, also known as -1 embedding, is used, the detection rates are considerably reduced. In particular, since LSB embedding is modeled as an additive noise process, detection is especially poor for images that exhibit high-frequency noise - the high-frequency noise is often incorrectly thought to be indicative of a hidden message. To overcome this, we propose a targeted steganalysis algorithm that exploits the fact that after LSB matching, the local maxima of an images graylevel or color histogram decrease and the local minima increase. Consequently, the sum of the absolute differences between local extrema and their neighbors in the intensity histogram of stego images will be smaller than for cover images. Experimental results on two datasets, each of 2000 images, demonstrate that this method has superior results compared with other recently proposed algorithms when the images contain high-frequency noise, e.g. never-compressed imagery such as high-resolution scans of photographs and video. However, the method is inferior to the prior art when applied to decompressed imagery with little or no high-frequency noise.