Developing dual attribute adversarial camouflage for counter-AI reconnaissance

Deep Learning based architectures such as Convolutional Neural Networks (CNNs) have become quite efficient in recent years at detecting camouflaged objects that would be easily overlooked by a human observer. Consequently, countermeasures have been developed in the form of adversarial attack patterns which can confuse CNNs by causing false classifications while maintaining the original camouflage properties in the visible spectrum. In this paper, we describe the various steps in generating suitable adversarial camouflage patterns based on the Dual Attribute Adversarial Camouflage (DAAC) technique for evading the detection by artificial intelligence as well as human observers which was proposed in [Wang et al. 2021]. The aim here is to develop an efficient camouflage with the added ability to confuse more than a single network without compromising camouflage against human observers. In order to achieve this, two different approaches are suggested and the results of first tests are presented.