  Universal Adversarial Perturbations (UAPs) are a prominent class of
adversarial examples that exploit the systemic vulnerabilities and enable
physically realizable and robust attacks against Deep Neural Networks (DNNs).
UAPs generalize across many different inputs; this leads to realistic and
effective attacks that can be applied at scale. In this paper we propose
HyperNeuron, an efficient and scalable algorithm that allows for the real-time
detection of UAPs by identifying suspicious neuron hyper-activations. Our
results show the effectiveness of HyperNeuron on multiple tasks (image
classification, object detection), against a wide variety of universal attacks,
and in realistic scenarios, like perceptual ad-blocking and adversarial
patches. HyperNeuron is able to simultaneously detect both adversarial mask and
patch UAPs with comparable or better performance than existing UAP defenses
whilst introducing a significantly reduced latency of only 0.86 milliseconds
per image. This suggests that many realistic and practical universal attacks
can be reliably mitigated in real-time, which shows promise for the robust
deployment of machine learning systems.
