Study of Adversarial Machine Learning with Infrared Examples for Surveillance Applications

Adversarial examples are theorized to exist for every type of neural network application. Adversarial examples have been proven to exist in neural networks for visual-spectrum applications and that they are highly transferable between such neural network applications. In this paper, we study the existence of adversarial examples for Infrared neural networks that are applicable to military and surveillance applications. This paper specifically studies the effectiveness of adversarial attacks against neural networks trained on simulated Infrared imagery and the effectiveness of adversarial training. Our research demonstrates the effectiveness of adversarial attacks on neural networks trained on Infrared imagery, something that hasn’t been shown in prior works. Our research shows that an increase in accuracy was shown in both adversarial and unperturbed Infrared images after adversarial training. Adversarial training optimized for the $L_{\infty }$ norm leads to an increase in performance against both adversarial and non-adversarial targets.