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.

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深度学习用于实时图像隐写分析：一项调查

隐写术是一种在适当的多媒体载体（例如图像，音频和视频文件）中传输秘密数据或消息的技术。它是基于以下假设：如果该功能可见，则攻击点显而易见。但是，不希望被轻易发现以在各种媒体（尤其是图像）中隐藏有害信息的罪犯总是使用这种技术。这些有害信息的大规模传播将增加社会保障管理的难度。在这种情况下，应开发和应用出色的图像隐写分析。特别地，当需要保护信息时间表时，需要进行实时图像隐写分析。如果检测场景具有大量用户，则可以利用其强大的处理能力来应用深度学习来提高图像隐写分析的性能。利用深度学习，实时图像隐写分析系统可以获得更高的准确性和效率。在本文中，我们首先介绍了初步知识。还简要介绍了深度神经网络（DNN）。介绍了DNN和实时图像隐写分析的结合。然后，将CNN的概念引入DNN中，阐述了CNN和图像隐写分析相结合的优点。对于多用户方案，我们基于异常值检测方法分析了实用的实时图像隐写分析应用程序。最后，我们展望了实时图像隐写分析的未来问题。介绍了DNN和实时图像隐写分析的结合。然后，将CNN的概念引入DNN中，阐述了CNN和图像隐写分析相结合的优点。对于多用户方案，我们基于异常值检测方法分析了实用的实时图像隐写分析应用程序。最后，我们展望了实时图像隐写分析的未来问题。介绍了DNN和实时图像隐写分析的结合。然后，我们将CNN的概念引入DNN中，并阐述了CNN和图像隐写分析相结合的优点。对于多用户方案，我们基于异常值检测方法分析了实用的实时图像隐写分析应用程序。最后，我们展望了实时图像隐写分析的未来问题。