Luo, P.; Liu, J.; Xu, J.; Dang, Q.; Mu, D. Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding. Remote Sens.2024, 16, 1331.
Luo, P.; Liu, J.; Xu, J.; Dang, Q.; Mu, D. Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding. Remote Sens. 2024, 16, 1331.
Luo, P.; Liu, J.; Xu, J.; Dang, Q.; Mu, D. Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding. Remote Sens.2024, 16, 1331.
Luo, P.; Liu, J.; Xu, J.; Dang, Q.; Mu, D. Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding. Remote Sens. 2024, 16, 1331.
Abstract
To ensure the security of highly sensitive remote sensing images (RSI) during their transmission, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on encoder-decoder network architecture with Parallel Attention Mechanisms (PAM) by adversarial training was proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN) and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSI and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.
Keywords
remote sensing image; distribution; deep information hiding; attention mechanism.
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
