Developing dual attribute adversarial camouflage for counter-AI reconnaissance

Claudia S. Hübner; Alexander Schwegmann

doi:10.1117/12.2679418

23 October 2023 Developing dual attribute adversarial camouflage for counter-AI reconnaissance

Proceedings Volume 12736, Target and Background Signatures IX; 1273604 (2023) https://doi.org/10.1117/12.2679418
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

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.

Conference Presentation

Citation Download Citation

Claudia S. Hübner and Alexander Schwegmann "Developing dual attribute adversarial camouflage for counter-AI reconnaissance", Proc. SPIE 12736, Target and Background Signatures IX, 1273604 (23 October 2023); https://doi.org/10.1117/12.2679418

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