Coverless image steganography based on DenseNet feature mapping

EURASIP Journal on Image and Video Processing

Xuyu Xiang¹  Qiang Liu¹  Jiaohua Qin¹  Yun Tan¹  Yao Qiu¹ 

[1] College of Computer Science and Information Technology, Central South University of Forestry and Technology;

关键词: Coverless image steganography;    Deep learning;    DenseNet convolutional neural network;    CNN features;   

DOI  :  10.1186/s13640-020-00521-7

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown