Low-complexity JPEG steganalysis via filters optimation from symmetric property

Abstract

Steganalysis is a technique for detecting existence of secret data hidden in digital media. Researchers are often concentrated in minimizing the misclassification rate. Many steganalysis methods try to choose high-dimension features to reveal subtle changes due to data embedding. By exploring properties of the filter bases, we steer the filters to capture tiny embedding traces in different scales and directions. There are redundant filters due to the horizontal and vertical symmetric properties. To select proper filters, the constructed framework selects a filter subset used in feature extraction and can improve the classification performance. The proposed algorithm is compared with several high-dimension features including Gabor filter residual (GFR) and maximum diversity cascade filter residual (MD-CFR) features. The following steganographic algorithms are used in the comparison experiment to test the steganalytic performance: uniform embedding revisited distortion (UERD) and JPEG universal wavelet relative distortion (J-UNIWARD). Experimental results show that, compared with the MD-CFR method, the proposed method can increase the detection rate by up to 0.7% with low feature dimension. It is shown that the proposed method is suitable for detecting data embedding in JPEG images.