Deep Learning in Military Applications
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Keywords

artifficial intelligence
Deep Learning
military applications

How to Cite

Surma, J. (2024). Deep Learning in Military Applications. Safety & Defense, 10(1), 1-7. https://doi.org/10.37105/sd.214

Abstract

The latest advancements in Artificial Intelligence, especially in Deep Learning technology, accelerate innovation and development in different application domains. The development of Deep Learning technology has profoundly impacted military development trends, leading to major changes in the forms and models of war. In this paper, we overview Deep Learning's history and architecture. Then, we review related work and extensively describe Deep Learning in two primary military applications: intelligence operations and autonomous platforms. Finally, we discuss related threats, opportunities, technical and practical difficulties. The main findings are that Artificial Intelligence technology is not omnipotent and needs to be applied carefully, considering its limitations, cybersecurity threats and a strong need for human supervision in the OODA decision loop. Certain safeguard mechanisms are required at the strategic decision-making level. In this context, one of the most important aspects relates to the education, training and selection of military officer personnel.

https://doi.org/10.37105/sd.214
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References

Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.

Das, S., Jain, L., & Das, A. (2018). Deep learning for military image captioning. In 2018 21st International Conference on Information Fusion (FUSION, IEEE, 2165-2171.

Denker, J., & LeCun, Y. (1990). Transforming neural-net output levels to probability distributions. Advances in neural information processing systems, 3.

Denton, E., Hanna, A., Amironesei, R., Smart, A., & Nicole, H. (2021). On the genealogy of machine learning datasets: A critical history of ImageNet. Big Data & Society, 8(2), 20539517211035955.

Einsidler, D., Dhanak, M., & Beaujean, P. P. (2018). A deep learning approach to target recognition in side-scan sonar imagery. In Oceans 2018 Mts/Ieee Charleston, IEEE, 1-4.

Fernandes, J. D. C. V., de Moura Junior, N. N., & de Seixas, J. M. (2022). Deep learning models for passive sonar signal classification of military data. Remote Sensing, 14(11), 2648.

Fukushima, K. (1988). Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural networks, 1(2), 119-130.

Hagström, M. (2019). Military applications of machine learning and autonomous systems. The impact of artificial intelligence on strategic stability and nuclear risk, 1, 33-38.

Hayir, N. (2022). Defining Weapon Systems with Autonomy: The Critical Functions in Theory and Practice. Groningen Journal of International Law, 9(2).

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.

Hyman, P. (2012). John McCarthy, 1927--2011. Communications of the ACM, 55(1), 28-29.

Jo, A. (2023). The promise and peril of generative AI. Nature, 614(1), 214-216.

Kisačanin, B. (2017). Deep learning for autonomous vehicles. In 2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL), IEEE, 142-142.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.

Kuutti, S., Bowden, R., Jin, Y., Barber, P., & Fallah, S. (2020). A survey of deep learning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems, 22(2), 712-733.

Layton, P. (2021). Fighting Artificial Intelligence Battles: Operational Concepts for Future AI-Enabled Wars. Network, 4(20), 1-100.

Long, K., & Zhu, Q. (2017). Algorithmic warfare: concept, characteristics and implications. National Defense Science & Technology, (6), 8.

Longpre, S., Storm, M., & Shah, R. (2022). Lethal autonomous weapons systems & artificial intelligence: Trends, challenges, and policies. Edited by Kevin McDermott. MIT Science Policy Review, 3, 47-56.

Masuhr, N. (2019). Ai in military enabling applications. CSS Analyses in Security Policy, 251.

Merkert, J., Mueller, M., & Hubl, M. (2015). A survey of the application of machine learning in decision support systems. Clin. Cancer Res., 5(2), 267-274.

Mohanty, S. P., Czakon, J., Kaczmarek, K. A., Pyskir, A., Tarasiewicz, P., Kunwar, S., & Schilling, M. (2020). Deep learning for understanding satellite imagery: An experimental survey. Frontiers in Artificial Intelligence, 3, 534696.

Morgan, F. E., Boudreaux, B., Lohn, A. J., Ashby, M., Curriden, C., Klima, K., & Grossman, D. (2020). Military applications of artificial intelligence. Santa Monica: RAND Corporation.

Neupane, D., & Seok, J. (2020). A review on deep learning-based approaches for automatic sonar target recognition. Electronics, 9(11), 1972.

Pritt, M., & Chern, G. (2017). Satellite image classification with deep learning. In 2017 IEEE applied imagery pattern recognition workshop (AIPR), IEEE, 1-7.

Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9), 2352-2449.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., & Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115, 211-252.

Seffers, G. I. (2016). Commanding the future mission. Signal, 70(9), 16-19.

Steiniger, Y., Kraus, D., & Meisen, T. (2022). Survey on deep learning based computer vision for sonar imagery. Engineering Applications of Artificial Intelligence, 114, 105157.

Surma, J. (2020). Hacking machine learning: towards the comprehensive taxonomy of attacks against machine learning systems. In Proceedings of the 2020 the 4th international conference on innovation in artificial intelligence , 1-4.

Surma, J. (2023). The Business dimension of Metaverse. Scientific Papers of Silesian University of Technology. Organization & Management (170).

Svenmarck, P., Luotsinen, L., Nilsson, M., & Schubert, J. (2018). Possibilities and challenges for artificial intelligence in military applications. In Proceedings of the NATO Big Data and Artificial Intelligence for Military Decision Making Specialists’ Meeting, 1-16.

Szabadföldi, I. (2021). Artificial intelligence in military application–opportunities and challenges. Land Forces Academy Review, 26(2), 157-165.

Tadjdeh, Y. (2019). DARPA’s ‘AI next’program bearing fruit. National Defense, 104(788), 8-8.

Vecherin, S. N., Desmond, J. R., Hodgdon, T. S., Bates, J. T., Parker, M. W., Lever, J. H., & Shoop, S. A. (2020). Artificial intelligence and machine learning for autonomous military vehicles.

Wang, W., Liu, H., Lin, W., Chen, Y., & Yang, J. A. (2020). Investigation on works and military applications of artificial intelligence. IEEE Access, 8, 131614-131625.

Yi-Ming, L., & Hai-Ming, S. (2016). Technology subduing: Analysis of the US third ‘Offset Strategy. J. Command Control, 2(2), 167-171.

Zhu, P., Isaacs, J., Fu, B., & Ferrari, S. (2017). Deep learning feature extraction for target recognition and classification in underwater sonar images. In 2017 IEEE 56th annual conference on decision and control (CDC), IEEE, 2724-2731.

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