Jarhinbek Rasol

Abstract : Deep learning-based algorithms for single object tracking (SOT) have shown impressive performance but remain susceptible to adversarial patch attacks. However, existing adversarial patch generation methods primarily focus on generating patches within the search region, neglecting the incorporation of template information, which limits their effectiveness in carrying out successful attacks. There is also a lack of evaluation metrics to assess the patch’s adversarial abilities.

Abstract : The “low, slow, and small” target (LSST) poses a significant threat to the military ground unit. It is hard to defend against due to its invisibility to numerous detecting devices. With the onboard deep learning-based object detection methods, the intelligent LSST (ILSST) can find and detect the ground unit autonomously in a denied environment. This paper proposes an adversarial patch-based defending method to blind the ILSST by attacking its onboard object detection network.

This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Qing Zhou , Yuelei Xu *, Jarhinbek Rasol , Tian Hui , Chaofeng Yuan and Fan Li Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China * Author to whom correspondence should be addressed.