Camouflaged communication using a media is known as Steganography. It is different than encryption as the presence of message is also concealed in case of steganography. The message however can be encrypted before hiding in a media. Detection of concealed exchange being carried out or unraveling the details of such transmission is known as Steganalysis. Steganalysis can be detected by classifying the given media file as cover media file or stego media file. Blind steganalysis detects presence of hidden content without any knowledge about the cover media file and steganography algorithm used. Steganalysis plays a vital role in forensics of various media such as text, audio, image, video and network packets. Machine learning techniques have been widely used for steganalysis in literature. These techniques use a three step approach consisting of Feature Extraction, Training and Testing Phases. Deep learning techniques, a subset of machine learning techniques are preferred by researchers over machine learning techniques as (i) they consist of Training and Testing Phases with the feature extraction step done automatically, (ii) they give better accuracy when trained with huge amount of data. This paper proposes novel multi class blind steganalysis technique for images. Convolutional Neural Network (CNN) is one of the best known architecture used with image steganalysis. But as the depth of CNN architecture increases, problem of vanishing descent arise which affects the accuracy. In order to solve the problem of the vanishing/exploding gradient in CNN, concept called Residual Network which use a technique called skip connections is being used. The skip connection skips training from few layers and connects directly to the output. A deep residual network helps to automatically capture complex statistical features of images and preserve weak stego signal in image content making it suitable for multi class blind steganalysis. This paper uses deep residual network for multi-class blind steganalysis. Proposed DRN has been successfully applied for multi class blind steganalysis in spatial and JPEG images. Experimental results demonstrate proposed network is comparable to state or art techniques present in literature.

使用媒体进行的伪装通信称为隐写术。它与加密不同，因为在密写的情况下也隐藏了消息的存在。但是，可以在隐藏媒体之前对消息进行加密。进行隐匿交换的检测或弄清这种传输的细节被称为隐写分析。可以通过将给定的媒体文件分类为封面媒体文件或隐蔽媒体文件来检测隐匿分析。盲隐式分析可以检测隐藏内容的存在，而无需了解所使用的封面媒体文件和隐写术算法。隐写分析在各种媒体（例如文本，音频，图像，视频和网络数据包）的取证中起着至关重要的作用。机器学习技术已被广泛用于文献中的隐写分析。这些技术使用三步法，包括特征提取，训练和测试阶段。深度学习技术是机器学习技术的一个子集，相对于机器学习技术，研究人员更喜欢：数据。本文提出了一种新颖的多类图像盲隐写分析技术。卷积神经网络（CNN）是用于图像隐写分析的最著名的体系结构之一。但是，随着CNN架构深度的增加，出现下降消失的问题，这会影响精度。为了解决CNN中消失/爆炸梯度的问题，正在使用一种称为残差网络的概念，该概念使用一种称为跳过连接的技术。跳过连接从几层跳过训练，并直接连接到输出。深度残差网络有助于自动捕获图像的复杂统计特征，并在图像内容中保留微弱的隐秘信号，使其适用于多类盲隐分析。本文将深度残差网络用于多类盲隐写分析。提议的DRN已成功应用于空间和JPEG图像的多类盲隐写分析。实验结果表明，所提出的网络可与文献中提到的先进技术相媲美。深度残差网络有助于自动捕获图像的复杂统计特征，并在图像内容中保留微弱的隐秘信号，使其适用于多类盲隐分析。本文将深度残差网络用于多类盲隐写分析。提议的DRN已成功应用于空间和JPEG图像的多类盲隐写分析。实验结果表明，所提出的网络可与文献中提到的先进技术相媲美。深度残差网络有助于自动捕获图像的复杂统计特征，并在图像内容中保留微弱的隐秘信号，使其适用于多类盲隐分析。本文将深度残差网络用于多类盲隐写分析。提议的DRN已成功应用于空间和JPEG图像的多类盲隐写分析。实验结果表明，所提出的网络可与文献中提到的先进技术相媲美。