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.

翻译：深度神经网络（DNNs）已被广泛应用于空中检测。然而，DNNs对恶意精心设计的对抗样本的敏感性和脆弱性逐渐引起关注。近年来，物理攻击因在真实世界中更具实用性而成为热点问题，这对一些安全关键应用构成巨大威胁。本文首次尝试在物理世界中以上下文形式对空中检测实施物理攻击。我们提出了一种创新的上下文攻击方法，用于真实场景中的空中检测，该方法在不涂抹或遮挡感兴趣目标的情况下，实现了强大的攻击性能，并在不同空中目标检测器之间具有良好的可迁移性。基于对检测器注意力图的观察，我们发现目标的上下文信息在空中检测中起着重要作用，因此提出充分利用感兴趣目标的上下文区域，精心设计上下文扰动，以实现真实场景中的无遮挡攻击。通过大规模比例缩放实验评估所提上下文攻击方法的有效性，结果表明该方法在攻击效能和物理实用性方面均具有优越性。