A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage

Feng Chen (), Qinghua Xing, Bing Sun, Xuehu Yan and Huan Lu
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Feng Chen: College of Air Defense and Anti-Missile, Air Force Engineering University, Xi’an 710051, China
Qinghua Xing: College of Air Defense and Anti-Missile, Air Force Engineering University, Xi’an 710051, China
Bing Sun: China Satellite Maritime Tracking and Control Department, Jiangyin 214430, China
Xuehu Yan: College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
Huan Lu: College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China

Mathematics, 2022, vol. 10, issue 19, 1-13

Abstract: Image data play an important role in our daily lives, and scholars have recently leveraged deep learning to design steganography networks to conceal and protect image data. However, the complexity of computation and the running speed have been neglected in their model designs, and steganography security still has much room for improvement. For this purpose, this paper proposes an RDA-based network, which can achieve higher security with lower computation complexity and faster running speed. To improve the hidden image’s quality and ensure that the hidden image and cover image are as similar as possible, a residual dense attention (RDA) module was designed to extract significant information from the cover image, thus assisting in reconstructing the salient target of the hidden image. In addition, we propose an activation removal strategy (ARS) to avoid undermining the fidelity of low-level features and to preserve more of the raw information from the input cover image and the secret image, which significantly boosts the concealing and revealing performance. Furthermore, to enable comprehensive supervision for the concealing and revealing processes, a mixed loss function was designed, which effectively improved the hidden image’s visual quality and enhanced the imperceptibility of secret content. Extensive experiments were conducted to verify the effectiveness and superiority of the proposed approach.

Keywords: security issue; information leakage; residual dense attention; activation removal strategy; imperceptibility (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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