中文摘要：

采用非公开的图像源或算法的隐写行为具有很强的隐蔽性．在这类对隐写者先验不足的场景下聚类分析更为实用．Ker等人比较不同指标不同配置之后，提出基于MMD指标聚类的隐写者识别方法．然而该方法所用MMD指标只考虑两个类样本中心之间的距离，忽略了样本相对中心点的聚合程度对可分性的影响，因而准确率存在提高的空间．为进一步提高现有隐写聚类分析方法的准确率，该文提出用核Fisher鉴别（KFI））指标计算样本间差异度量的聚类方法．首先，提取PEV274校准特征并归一化．然后，计算KFD指标组成距离矩阵．最后，根据样本间差异度量矩阵按重心法自底向上进行层次聚类分析．KFD指标兼顾与最大平均距离（MMD）原理相近的类间方差以及指示样本聚集程度的类内方差，更准确地估算样本间差异．实验结果表明，该文对低嵌入率隐写其准确率最高提高约30％，对高嵌入率准确率降低不超过5％．该文的创新点在于提出了一种更合理的指标和基于该指标聚类隐写分析的方法，比现有方法平均准确率有一定的提高．

英文摘要：

It is highly undetectable of steganographers who avoid utilizing public sources of ima- ges or steganographic schemes. In such scenario where steganalysers have little priori knowledge about steganographers, clustering is more practical. Ker proposed a MMD-based clustering scheme to distinguish steganographers from innocent actors after comparisons in various configu- rations and indexes. MMD merely considers the distance between centers of samples from two classes, but ignores the fact that aggregation how samples gather around their centers does affect the separability. Hence, its accuracy needs improvement. To increase the detecting rate further, we propose a clustering based steganalytic scheme using kernel Fisher discriminant indexes （KFDI） as the dissimilarities of samples. We firstly extract the calibration features PE;V274 and have them normalized. Then, we calculate the KFD indexes between samples to form the distance matrix. Finally, hierarchical clustering is proceeded with bottom-up iteration ＇where we used thecenter of gravity as the center for the new gathered clusters. KFDI considers not only between- class variances that maximum mean discrepancy concentrates on, but also within-class variance that affects the aggregation between classes. Experimental results show that our scheme obtains a high increase in accuracy under low embedding rates, about 30% at most, but a little decrease of no more than 5 % under high embedding rates. The key contribution of this paper is to propose a more reasonable indicators and steganalytic method based on the KFDI, and we raised the average accuracy of existing methods.center of gravity as the center for the new gathered clusters. KFDI considers not only between- class variances that maximum mean discrepancy concentrates on, but also within-class variance that affects the aggregation between classes. Experimental results show that our scheme obtains a high increase in accuracy under low embedding rates, about 30% at most, but a little decrease of no more than