Coverless image steganography based on DenseNet feature mapping
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(2020) 2020:39
EURASIP Journal on Image and Video Processing
RESEARCH
Open Access
Coverless image steganography based on DenseNet feature mapping Qiang Liu1 , Xuyu Xiang2,1* *Correspondence: [email protected] 1 College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China 2 College of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
, Jiaohua Qin1 , Yun Tan1 and Yao Qiu1
Abstract Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks. Keywords: Coverless image steganography, Deep learning, DenseNet convolutional neural network, CNN features
1 Introduction Information hiding is the most common way to protect secret information. Information encryption is the earliest means of protecting secret information, and it is using computer encryption to change the digital structure of load information in digital communication. However, the encryption technology is easy to be detected, it cannot ensure confidentiality of information, and the computational complexity is high. Therefore, researchers began to use image steganography to realize the secret transmission of important information, and it is mainly embedding the secret information into the carrier. It keeps the maximum visual similarity between the carrier and the original object, so as to avoid the abnormalities during transmission process. In the last few decades, many image steganography approaches [1–8] have been proposed. However, most of them embed the hidden secret information into the carrier to replace the hidden secret information in the pixels, which can be easily detected by steganographic analysis tools [9, 10]. Therefore, how to hide information effectively without modifying the carrier is a breakthrough and challenging point. © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format,
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