In practical cloud-edge scenarios, where a resource constrained edge performs
data acquisition and a cloud system (having sufficient resources) performs
inference tasks with a deep neural network (DNN), adversarial robustness is
critical for reliability and ubiquitous deployment. Adversarial detection is a
prime adversarial defence technique used in prior literature. However, in prior
detection works, the detector is attached to the classifier model and both
detector and classifier work in tandem to perform adversarial detection that
requires a high computational overhead which is not available at the low-power
edge. Therefore, prior works can only perform adversarial detection at the
cloud and not at the edge. This means that in case of adversarial attacks, the
unfavourable adversarial samples must be communicated to the cloud which leads
to energy wastage at the edge device. Therefore, a low-power edge-friendly
adversarial detection method is required to improve the energy efficiency of
the edge and robustness of the cloud-based classifier. To this end, RobustEdge
proposes Quantization-enabled Energy Separation (QES) training with "early
detection and exit" to perform edge-based low cost adversarial detection. The
QES-trained detector implemented at the edge blocks adversarial data
transmission to the classifier model, thereby improving adversarial robustness
and energy-efficiency of the Cloud-Edge system.

在云边场景中，为了可靠性和普遍部署，对于资源受限的边缘进行数据采集，而云系统（具有足够资源）进行深度神经网络的推理任务，对抗鲁棒性至关重要。为了提高边缘的能效和云端的鲁棒性，提出了一种适用于低功耗边缘的对抗检测方法。