Deep learning for real-time image steganalysis: a survey

Steganography is a technique that transmits secret data or message in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident. However, such technology is always used by criminals who do not want to be easily discovered to hide harmful information in various media, especially in images. Massive spreading of those harmful information will increase the difficulty of social security management. In this case, excellent image steganalysis should be developed and applied. Specially, real-time image steganalysis is necessary when information timelines need to be protected. If detection scene has large amounts of users, deep learning can be applied to improve performance of image steganalysis benefiting from its powerful processing capability. Using deep learning, real-time image steganalysis system gets higher accuracy and efficiency. In this paper, we give an account of preliminary knowledge first. A brief overview of the deep neural networks (DNN) is also presented. The combination of DNN and real-time image steganalysis is introduced. Then, we import the concept of CNN in DNN, and expound theory as well as advantages of combining CNN and image steganalysis. For multi-user scenarios, we analyze a practical real-time image steganalysis application based on outlier detection methods. At last, we prospect the future issues of real-time image steganalysis.