Space filling curves in steganalysis

We introduce a new method to increase the reliability of current steganalytic techniques by optimising the sample order. Space filling curves (e. g., Hilbert curve) take advantage of the correlation of adjacent pixels and thus make the detection of steganographic messages with low change densities more reliable. The findings are applicable, but not limited to LSB steganalysis. An experimental comparison of five different sampling paths reveals that recursive principles achieve by far the best performance. All measures, such as mean distance, median autocorrelation, and the ability to detect even tiny modifications show substantial improvements compared to conventional methods. We elaborate the relationship between those parameters and quantify the effectiveness with a large test database of small images, which are usually hard to detect. Apart from quantitative advances, visualisation of steganalytic measures can also gain from the application of reverse space filling curves.