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BASN—Learning Steganography with a Binary Attention Mechanism

by Pin Wu 1, Xuting Chang 1, Yang Yang 1 and Xiaoqiang Li 1,2,*
1
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2
Shanghai Institute for Advanced Communication & Data Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Future Internet 2020, 12(3), 43; https://doi.org/10.3390/fi12030043
Received: 15 January 2020 / Revised: 22 February 2020 / Accepted: 24 February 2020 / Published: 27 February 2020
Secret information sharing through image carriers has aroused much research attention in recent years with images’ growing domination on the Internet and mobile applications. The technique of embedding secret information in images without being detected is called image steganography. With the booming trend of convolutional neural networks (CNN), neural-network-automated tasks have been embedded more deeply in our daily lives. However, a series of wrong labeling or bad captioning on the embedded images has left a trace of skepticism and finally leads to a self-confession like exposure. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and, in the meantime, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms. View Full-Text
Keywords: convolutional neural network; steganography; attention mechanism convolutional neural network; steganography; attention mechanism
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Wu, P.; Chang, X.; Yang, Y.; Li, X. BASN—Learning Steganography with a Binary Attention Mechanism. Future Internet 2020, 12, 43.

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