提出一种新的结合重压缩检测的JPEG图像多类隐写分析方法，实现一次压缩和重压缩图像中多种隐写算法的识别. 首先基于DCT 系数首位数分布规律，提出一种重压缩检测方法，然后从系数直方图、块内相关性、块间相关性和空域块效应中提取盲检测特征用于隐写分析，最后用支持向量机构造JPEG 隐写算法多类检测器.
实验结果表明，本文方法的重压缩检测性能明显优于已有方法，且对嵌入改变量的鲁棒性较强，隐写分析特征不仅维数较低而且具有更好的检测性能，构造的多类隐写分析器能较好地识别JPEG 隐写算法.

A novel multi-class steganalyzer with recompression detection for JPEG images is proposed to identify steganographic methods from singly and doubly compressed stego images. Based on the statistical distribution of the first digits of DCT coefficients, a JPEG recompression detection method is proposed. Features for blind detection are extracted from the histogram, intrablock correlation, interblock correlation and spatial blockiness. Finally, a multi-class detector against current steganographic methods is constructed with support vector machine. The experimental results show that the proposed recompression detection scheme outperforms the existing methods significantly, and is robust to embedding changes. The low dimensional
steganalytic features have better performance, and the multi-class steganalyzer can identify the current JPEG steganographic methods reliably.