Steganalysis aided by fragile detection of image manipulations

Steganalysis is usually considered as a two-class classification problem of differentiating between covers and stegos. However, in the real world, the cover image may have undergone various operations, which causes two problems that some processed covers tend to be judged as stegos by the steganalyzer and the stegos processed before information embedding may be easily missed, resulting in the high false alarm rate and the high missed detection rate of steganalysis respectively. To address the former problem, this paper proposed a steganalysis framework based on the combination of the image forensics and the steganalysis tools to reduce the false alarms. First, the fragile detection of image manipulations which is not robust to steganography is applied to separate the normally processed images from the investigated images. Then remaining images are fed to the trained classifier for stegnalysis. The experimental results on gamma transformed images validate the effectiveness of the proposed steganalysis framework that the false alarm rates of steganalysis can be reduced when the investigated image dataset contains normally processed images.