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.
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.
Infrared (IR) imagery is frequently used in security/surveillance and military image processing applications. In this article we will consider the problem of outlining military naval vessels in such images. Obtaining these outlines is important for a number of applications, for instance in vessel classification.
Detecting this outline is basically a very complex image segmentation task. We will use a special neural network for this purpose. Neural networks have recently shown great promise in a wide range of image processing applications, image segmentation is no exception in this regard. The main drawback when using neural networks for this purpose is the need for substantial amounts of data in order to train the networks. This problem is of particular concern for our application due to the difficulty in obtaining IR images of military vessels.
In order to alleviate this problem we have experimented with using alternatives to true IR images for the training of the neural networks. Although such data in no way can capture the exact nature of real IR images, they do capture the nature of IR images to a degree where they contribute substantially to the training and final performance of the neural network.