{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:45Z","timestamp":1760144325657,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T00:00:00Z","timestamp":1712707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62372069","62272478","6217072522"],"award-info":[{"award-number":["62372069","62272478","6217072522"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013decoder 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.<\/jats:p>","DOI":"10.3390\/rs16081331","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T10:55:28Z","timestamp":1712746528000},"page":"1331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3370-4191","authenticated-orcid":false,"given":"Peng","family":"Luo","sequence":"first","affiliation":[{"name":"School of Cybersecurity, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"School of Cryptography Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8104-0079","authenticated-orcid":false,"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Cryptography Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"given":"Jingting","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Qian","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Cryptography Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"given":"Dejun","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.3390\/rs14246371"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, G., Huang, X., and Poslad, S. (2022). Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation. Remote Sens., 14.","DOI":"10.3390\/rs14215574"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.3390\/rs15041022"},{"key":"ref_4","unstructured":"Naman, S., Bhattacharyya, S., and Saha, T. (2020). Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8898","DOI":"10.1016\/j.ijhydene.2022.11.294","article-title":"Accelerate oxygen evolution reaction by adding chemical mediator and utilizing solar energy","volume":"48","author":"He","year":"2023","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_6","first-page":"5","article-title":"A Survey on Digital Data Hiding Schemes: Principals, Algorithms, and Applications","volume":"5","author":"Akhaee","year":"2013","journal-title":"ISeCure"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3382772","article-title":"Data hiding: Current trends, innovation and potential challenges","volume":"16","author":"Singh","year":"2020","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_8","unstructured":"Rehman, A., Rahim, R., Nadeem, M., and Hussain, S. (2018, January 8\u201314). End-to-end trained CNN encode-decoder networks for image steganography. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany."},{"key":"ref_9","unstructured":"Zhang, K.A., Cuesta-Infante, A., Xu, L., and Veeramachaneni, K. (2019). SteganoGAN: High Capacity Image Steganography with GANs. arXiv."},{"key":"ref_10","unstructured":"Yu, C. (2020, January 7\u201312). Attention Based Data Hiding with Generative Adversarial Networks. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21966","DOI":"10.1109\/ACCESS.2020.2969524","article-title":"Technology of hiding and protecting the secret image based on two-channel deep hiding network","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4577","DOI":"10.1109\/TII.2021.3123233","article-title":"Multilevel Strong Auxiliary Network for Enhancing Feature Representation to Protect Secret Images","volume":"18","author":"Chen","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_13","first-page":"1","article-title":"Remote Sensing Scene Classification Based on Multibranch Fusion Attention Network","volume":"20","author":"Shi","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/LSP.2006.870357","article-title":"LSB matching revisited","volume":"13","author":"Mielikainen","year":"2006","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TIFS.2011.2134094","article-title":"Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes","volume":"6","author":"Filler","year":"2011","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_18","unstructured":"Pevn\u1ef3, T., Filler, T., and Bas, P. (2010, January 28\u201330). Using high-dimensional image models to perform highly undetectable steganography. Proceedings of the Information Hiding: 12th International Conference, IH 2010, Calgary, AB, Canada. Revised Selected Papers 12."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Holub, V., and Fridrich, J. (2012, January 2\u20135). Designing steganographic distortion using directional filters. Proceedings of the 2012 IEEE International Workshop on Information Forensics and Security (WIFS), Tenerife, Spain.","DOI":"10.1109\/WIFS.2012.6412655"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Holub, V., and Fridrich, J. (2013, January 17\u201319). Digital image steganography using universal distortion. Proceedings of the first ACM Workshop on Information Hiding and Multimedia Security, Montpellier, France.","DOI":"10.1145\/2482513.2482514"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, B., Wang, M., Huang, J., and Li, X. (2014, January 27\u201330). A new cost function for spatial image steganography. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025854"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, J., Kaplan, R., Johnson, J., and Fei-Fei, L. (2018, January 8\u201314). Hidden: Hiding data with deep networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_40"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TPAMI.2019.2901877","article-title":"Hiding Images within Images","volume":"42","author":"Baluja","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9314","DOI":"10.1109\/ACCESS.2019.2891247","article-title":"Reversible image steganography scheme based on a U-Net structure","volume":"7","author":"Duan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5018417","DOI":"10.1109\/TIM.2023.3285981","article-title":"ARWGAN: Attention-guided Robust Image Watermarking Model Based on GAN","volume":"72","author":"Huang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/TNSE.2021.3139671","article-title":"Channel attention image steganography with generative adversarial networks","volume":"9","author":"Tan","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, F., Xing, Q., Sun, B., Yan, X., and Cheng, J. (2022). An Enhanced Steganography Network for Concealing and Protecting Secret Image Data. Entropy, 24.","DOI":"10.3390\/e24091203"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/LSP.2016.2548421","article-title":"Structural Design of Convolutional Neural Networks for Steganalysis","volume":"23","author":"Xu","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_35","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR.org, Sydney, Australia."},{"key":"ref_36","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2023, November 05). Improved Training of Wasserstein GANs. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/hash\/892c3b1c6dccd52936e27cbd0ff683d6-Abstract.html."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Almohammad, A., and Ghinea, G. (2010, January 26\u201329). Stego image quality and the reliability of PSNR. Proceedings of the International Conference on Image Processing, Hong Kong, China.","DOI":"10.1109\/IPTA.2010.5586786"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","unstructured":"Boehm, B. (2014). Stegexpose-A tool for detecting LSB steganography. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yedroudj, M., Comby, F., and Chaumont, M. (2018, January 15\u201320). Yedroudj-net: An efficient CNN for spatial steganalysis. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461438"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1331\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:25:44Z","timestamp":1760106344000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1331"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,10]]},"references-count":40,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081331"],"URL":"https:\/\/doi.org\/10.3390\/rs16081331","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,4,10]]}}}