A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage
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 secu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/19/3501 |
| _version_ | 1797478193228152832 |
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| author | Feng Chen Qinghua Xing Bing Sun Xuehu Yan Huan Lu |
| author_facet | Feng Chen Qinghua Xing Bing Sun Xuehu Yan Huan Lu |
| author_sort | Feng Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:28:31Z |
| format | Article |
| id | doaj.art-650a1c9725b44fecb8d014cec431c451 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T21:28:31Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-650a1c9725b44fecb8d014cec431c4512023-11-23T21:02:36ZengMDPI AGMathematics2227-73902022-09-011019350110.3390/math10193501A Novel RDA-Based Network to Conceal Image Data and Prevent Information LeakageFeng Chen0Qinghua Xing1Bing Sun2Xuehu Yan3Huan Lu4College of Air Defense and Anti-Missile, Air Force Engineering University, Xi’an 710051, ChinaCollege of Air Defense and Anti-Missile, Air Force Engineering University, Xi’an 710051, ChinaChina Satellite Maritime Tracking and Control Department, Jiangyin 214430, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaImage 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.https://www.mdpi.com/2227-7390/10/19/3501security issueinformation leakageresidual dense attentionactivation removal strategyimperceptibility |
| spellingShingle | Feng Chen Qinghua Xing Bing Sun Xuehu Yan Huan Lu A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage Mathematics security issue information leakage residual dense attention activation removal strategy imperceptibility |
| title | A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage |
| title_full | A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage |
| title_fullStr | A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage |
| title_full_unstemmed | A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage |
| title_short | A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage |
| title_sort | novel rda based network to conceal image data and prevent information leakage |
| topic | security issue information leakage residual dense attention activation removal strategy imperceptibility |
| url | https://www.mdpi.com/2227-7390/10/19/3501 |
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