A novel steganalysis framework of heterogeneous images based on GMM clustering

作者：

Highlights：

• Considering and utilizing the variability of statistical properties of different images;

• Propose a novel steganalysis framework based on Gaussian mixture model (GMM) clustering, targeting at heterogeneous images with different texture complexity;

• There are two main improvements compared to the current steganalysis frameworks;

• Propose the texture feature for clustering by LLT;

• The efficiency of the proposed framework is proved theoretically and experimentally.

摘要

•Considering and utilizing the variability of statistical properties of different images;•Propose a novel steganalysis framework based on Gaussian mixture model (GMM) clustering, targeting at heterogeneous images with different texture complexity;•There are two main improvements compared to the current steganalysis frameworks;•Propose the texture feature for clustering by LLT;•The efficiency of the proposed framework is proved theoretically and experimentally.

论文关键词：Steganalysis,Steganography,Clustering,Gaussian mixture model,Texture complexity

论文评审过程：Received 4 May 2013, Revised 4 December 2013, Accepted 16 January 2014, Available online 30 January 2014.

论文官网地址：https://doi.org/10.1016/j.image.2014.01.006