A comparative study of ± steganalyzers

We compare the performance of three steganalysis system for detection of plusmn1 steganography. We examine the relative performance of each system on three commonly used image databases. Experimental results clearly demonstrate that both absolute and relative performance of all three algorithms vary considerably across databases. This sensitivity suggests that considerably more work is needed to develop databases that are more representative of diverse imagery. In addition, we investigate how performance varies based on a variety of training and testing assumptions, specifically (i) that training and testing are performed for a fixed and known embedding rate, (ii) training is performed at one embedding rate, but testing is over a range of embedding rates, (iii) training and testing are performed over a range of embedding rates. As expected, experimental results show that performance under (ii) and (iii) is inferior to (i). The experimental results also suggest that test results for different embedding rates should not be consolidated into a single score, but rather reported separately. Otherwise, good performance at high embedding rates may mask poor performance at low embedding rates.