中文摘要：

传统隐写分析所需的隐写算法、嵌入率和图像来源等先验知识在实用中很难满足,上述条件未知的盲隐写分析场景下,使用聚类分析方法可以有效区分隐写者与非隐写者。设计一种适合所选特征的融合方案,用以提高JPEG聚类隐写分析的准确率,将偏序Markov模型特征的主成分与校准特征融合,充分利用特征互补并降低冗余,可以在参与者中更好地识别出隐写者,从而提高识别准确率。实验结果表明,在不同隐写算法和嵌入率条件下,采用该方法比现有方法准确率平均提高约2%,最高提高约16%。

英文摘要：

The prior knowledge for traditional steganalysis,such as steganography algorithms,embedding rates and sources of images,etc.,is difficult to be satisfied in practice.In the scenario of blind steganalysis that the above conditions are unknown,analysis using clustering can effectively distinguish between the actor who performs steganography and the others.We propose a method for fusion which is suitable for the selected features,and is to improve the accuracy of JPEG＇s steganalysis via clustering.It fuses the principal components of the feature based on partially ordered Markov models with the feature based on calibration,and makes full use of complementarity between features as well as reduces the redundancy,identifies out of the guilty actor better and improves the accuracy of identifying actors who perform steganography.Experimental results show that by different steganography approaches and in different embedding rate conditions,using our scheme can obtain a general increase in the accuracy of JPEG steganalysis by about 2% compared to the existing methods,and get a highest accuracy up to 16%.