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.

隐写分析是一种用于检测隐藏在数字媒体中的秘密数据的存在的技术。研究人员通常集中在最小化错误分类率上。许多隐写分析方法试图选择高维特征以揭示由于数据嵌入而引起的细微变化。通过探索滤光片底座的属性，我们指导滤光片捕获不同比例和方向的微小嵌入轨迹。由于水平和垂直对称特性，因此存在多余的滤波器。为了选择合适的过滤器，构造的框架选择用于特征提取的过滤器子集，并可以提高分类性能。将该算法与包括Gabor滤波器残差（GFR）和最大分集级联滤波器残差（MD-CFR）在内的几个高维特征进行了比较。在比较实验中，使用以下隐写算法来测试隐写性能：均匀嵌入重新访问失真（UERD）和JPEG通用小波相对失真（J-UNIWARD）。实验结果表明，与MD-CFR方法相比，该方法在特征尺寸较小的情况下，可将检测率提高多达0.7％。结果表明，该方法适用于检测嵌入在JPEG图像中的数据。