  Deep Neural Networks (DNNs) have been extensively utilized in aerial
detection. However, DNNs' sensitivity and vulnerability to maliciously
elaborated adversarial examples have progressively garnered attention.
Recently, physical attacks have gradually become a hot issue due to they are
more practical in the real world, which poses 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 to hide. Based on the findings that the
targets' contextual information plays an important role in aerial detection by
observing the detectors' attention maps, we propose to make full use of the
contextual area of the interested targets to elaborate contextual perturbations
for the uncovered attacks in real scenarios. Extensive proportionally scaled
experiments are conducted to evaluate the effectiveness of the proposed
contextual attack method, which demonstrates the proposed method's superiority
in both attack efficacy and physical practicality.
