Affiliation:
1. School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
2. School of Cryptography Engineering, Engineering University of PAP, Xi’an 710086, China
Abstract
To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, 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 the 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 an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is 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 RSIs 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.
Funder
National Natural Science Foundation of China
Reference40 articles.
1. Zhang, D., Ren, L., Shafiq, M., and Gu, Z. (2022). A Lightweight Privacy-Preserving System for the Security of Remote Sensing Images on IoT. Remote Sens., 14.
2. Zhang, X., Zhang, G., Huang, X., and Poslad, S. (2022). Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation. Remote Sens., 14.
3. Alsubaei, F.S., Alneil, A.A., Mohamed, A., and Mustafa Hilal, A. (2023). Block-Scrambling-Based Encryption with Deep-Learning-Driven Remote Sensing Image Classification. Remote Sens., 15.
4. Naman, S., Bhattacharyya, S., and Saha, T. (2020). Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018, Springer.
5. Accelerate oxygen evolution reaction by adding chemical mediator and utilizing solar energy;He;Int. J. Hydrogen Energy,2023