IH-SESD: Modeling Information Hiding With Super-Resolution Enhancement and Significant Region Detection for UAV Networks
Li, Mianjie Cui, Haozheng Shan, Chun Du, Xiaojiang Guizani, Mohsen
Li, Mianjie
Cui, Haozheng
Shan, Chun
Du, Xiaojiang
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Research Projects
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Abstract
The advent of unmanned aerial vehicle (UAV) networks, renowned for their expansive coverage capabilities and heightened adaptability, presents a promising landscape for bolstering the efficacy of Internet of Things (IoT) data transmissions. Nevertheless, the integration of UAVs into IoT ecosystems introduces a spectrum of security challenges, notably data tampering, man-in-the-middle (MitM) attacks, and eavesdropping, which threaten the integrity and confidentiality of transmitted information. Since covert transmission has the characteristics of strong concealment and difficult detection, UAV networks based on information hiding become a new paradigm for solving these security problems. This article proposes an information hiding algorithm based on super-resolution enhancement and significant region detection (IH-SESD). This algorithm incorporates the super-resolution enhanced SRCNN and the U2Net salient region detection technologies. Comprehensive performance analysis and comparisons with existing methods demonstrate the superiority of the proposed IH-SESD algorithm.
Citation
M. Li, H. Cui, C. Shan, X. Du and M. Guizani, "IH-SESD: Modeling Information Hiding With Super-Resolution Enhancement and Significant Region Detection for UAV Networks," in IEEE Internet of Things Journal, vol. 12, no. 8, pp. 9383-9390, 15 April15, 2025, doi: 10.1109/JIOT.2024.3470130
Source
IEEE INTERNET OF THINGS JOURNAL
Conference
Keywords
Information hiding, Internet of Things (IoT), Significant area detection, Super-resolution enhancement, Unmanned Aerial Vehicle (UAV) networks
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Source
Publisher
IEEE