图像LSB匹配隐藏的预分类检测模型

Using pre-classification to attack LSB matching

当前针对图像LSB匹配隐藏的各种检测方法跨数据集测试性能不佳,其检测效果对于JPEG压缩的和未压缩的图像往往差别很大。对两类图像的颜色空间分布进行了分析,利用量化的DCT交流系数的最高有效位重压缩后不再服从广义Benford定律的现象,查明图像的压缩历史,进而根据图像的通道数以及是否被压缩分别采取不同算法或相同算法的不同参数集合进行检测。实验结果表明,基于该检测模型能够有效提升算法的跨数据集检测能力,增强隐藏检测算法的实用性。

Most steganalysis methods for image LSB matching suffer from a drop in the performance when applied to different image databases, especially for JPEG-eompressed and uncompressed covers, which is also the main restriction to prevent steganalysis from practical usage. In this paper, we specify, the JPEG compression of a bitmap by taking advantage of the quantized DCT alternating coefficients, which no longer obey the Benford＇ s law very well. Then variations of steganalysis methods and parameter sets are exploited according to the number of channels and whether the image has been compressed before. The algorithms＇ performances get a great boost by the proposed working mode.