Contextual Adversarial Attack Against Aerial Detection in The Physical World

Jiawei Lian, Xiaofei Wang, Yuru Su, Mingyang Ma, Shaohui Mei

Abstract: Deep Neural Networks (DNNs) have been extensively utilized in aerial detection. However, DNNs are susceptible and vulnerable to adversarial examples Recently, physical attacks have gradually garnered attention due to their effectiveness and practicality, which pose great threats to some security-critical applications. In this paper, we take the first attempt to perform physical attacks in contextual form against aerial detection in the physical world. We propose an innovative contextual attack method against aerial detection in real scenarios, which achieves powerful attack performance and transfers well between various aerial object detectors without smearing or blocking the interested objects. Based on the findings that the targets’ contextual information plays an important role in aerial detection by observing the detectors’ attention maps, we fully use the contextual feature of the interested targets to elaborate background perturbations for the uncovered attacks in physical scenarios. Experiments with proportional scaling are conducted to evaluate the effectiveness of the proposed method, demonstrating its superiority in terms of both attack efficacy and physical practicality.