Content-dependent feature selection for block-based image steganalysis
Block-based image steganalysis, which conducts steganalysis on smaller homogenous blocks of a given test image, was proposed to improve the performance of steganalysis. However, the computational complexity of block-based image steganalysis is high when the feature size is large. To reduce the complexity, we develop a content-dependent feature selection scheme for a binary classifier. The main idea is to select important features depending on block types, which explains the term of “content-dependent” selection. This choice enables us to obtain better performance with a smaller number of features and reduce computational complexity at the same time. Experimental results are conducted to demonstrate the performance improvement of content-dependent feature selection with high detection accuracy.