Steganalysis of content-adaptive JPEG steganography based on scale co-occurrence matrix with diverse quantization

Abstract

Feature extraction is very important for steganalysis of content-adaptive JPEG steganography. The scale co-occurrence matrix feature based on a two-dimensional (2-D) Gabor filter is proposed, and diverse quantization for filter residuals is utilized to improve the detection performance. First, the definition of scale co-occurrence matrix based on a 2-D Gabor filter is given and the rules for feature merge are analyzed. Then, the influence of the scale parameter and quantization step on the detection performance of the scale co-occurrence matrix feature is discussed and verified. Next, the effect of diverse quantization strategy is presented. Last, the detailed extraction process of the proposed steganalysis feature is described. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms.