A Heuristic Feature Combination Selection Method in Fusion Detection of JPEG Stegoimages

The traditional technology of JPEG image steganography blind detection is implemented by a single feature or the fusion of two features. There has been no published research yet considering how to select a superior feature combination to achieve better detection performance. Here we get ideas from Hoffmann trees, put forward a superior feature combination selection method based on treelike structure, which selects correlation coefficient of canonical correlation analysis as its heuristic factor. In the method we try to fuse two feature combinations which have the least correlation coefficient every time, and remain the feature combination with higher accuracy rate. Finally, the superior feature combination can be obtained in the way. Experiments indicate that our method can get a feature combination which holds better fusion performance in limited computing complexity condition when we deal with steganography blind detection for JPEG images.