Multi-class blind steganalysis using deep residual networks

Abstract

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.

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Acknowledgments

We seriously thank the editor and anonymous reviewers for their valuable comments and suggestions which have immensely helped in improving the quality of our paper.

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Correspondence to Anuradha Singhal.

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Singhal, A., Bedi, P. Multi-class blind steganalysis using deep residual networks. Multimed Tools Appl (2021). https://doi.org/10.1007/s11042-020-10353-2

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Keywords

  • Steganalysis
  • Convolution neural networks
  • Deep residual networks
  • Bottleneck residual blocks