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Different types of imaging sensors are frequently employed for detection, tracking and classification (DTC) of naval vessels. A number of countermeasure techniques are currently employed against such sensors, and with the advent of ever more sensitive imaging sensors and sophisticated image analysis software, the question becomes what to do in order to render DTC as hard as possible. In recent years, progress in deep learning, has resulted in algorithms for image analysis that often rival human beings in performance. One approach to fool such strategies is the use of adversarial camouflage (AC). Here, the appearance of the vessel we wish to protect is structured in such a way that it confuses the software analyzing the images of the vessel. In our previous work, we added patches of AC to images of frigates. The paches were placed on the hull and/or superstructure of the vessels. The results showed that these patches were highly effective, tricking a previously trained discriminator into classifying the frigates as civilian. In this work we study the robustness and generality of such patches. The patches have been degraded in various ways, and the resulting images fed to the discriminator. As expected, the more the patches are degraded, the harder it becomes to fool the discriminator. Furthermore, we have trained new patch generators, designed to create patches that will withstand such degradations. Our initial results indicate that the robustness of AC patches may be increased by adding degrading flters in the training of the patch generator.
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