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19 September 2019 Adversarial camouflage for naval vessels
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Abstract
The use of different types of camouflage is a longstanding technique employed by armed forces in order to avoid detection, classification or tracking of objects of military interest. Typically, the use of such camouflage is intended to fool human observers. However, in future battle theaters one must expect to face weapons that are ’artificially intelligent’ in some way, and the question then arises as to whether the same types of camouflage will be effective against such weapons. An equally important question is if it is possible to design camouflage in order to specifically confuse ’artificially intelligent’ adversaries and what such camouflage might look like. It is this latter question that is the object of the study reported here. In particular, we consider whether carefully designed patterns of camouflage will have a detrimental effect on the performance of neural networks trained to distinguish among different ship classes. We train a neural network to distinguish between different types of military and civilian vessels and specifically require the network to determine whether the vessel is military or civilian. We then use this network to train a second network, a generative adversarial network, that will generate patterns to overlay on parts of the vessels in such a way as to thwart the performance of the first network. We show that such adversarial camouflage is very effective in confusing the original classification network.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lars Aurdal, Kristin Hammarstrøm Løkken, Runhild Aae Klausen, Alvin Brattli, and Hans Christian Palm "Adversarial camouflage for naval vessels", Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690K (19 September 2019); https://doi.org/10.1117/12.2532756
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