  Given their substantial success in addressing a wide range of computer vision
challenges, Convolutional Neural Networks (CNNs) are increasingly being used in
smart home applications, with many of these applications relying on the
automatic recognition of human activities. In this context, low-power radar
devices have recently gained in popularity as recording sensors, given that the
usage of these devices allows mitigating a number of privacy concerns, a key
issue when making use of conventional video cameras. Another concern that is
often cited when designing smart home applications is the resilience of these
applications against cyberattacks. It is, for instance, well-known that the
combination of images and CNNs is vulnerable against adversarial examples,
mischievous data points that force machine learning models to generate wrong
classifications during testing time. In this paper, we investigate the
vulnerability of radar-based CNNs to adversarial attacks, and where these
radar-based CNNs have been designed to recognize human gestures. Through
experiments with four unique threat models, we show that radar-based CNNs are
susceptible to both white- and black-box adversarial attacks. We also expose
the existence of an extreme adversarial attack case, where it is possible to
change the prediction made by the radar-based CNNs by only perturbing the
padding of the inputs, without touching the frames where the action itself
occurs. Moreover, we observe that gradient-based attacks exercise perturbation
not randomly, but on important features of the input data. We highlight these
important features by making use of Grad-CAM, a popular neural network
interpretability method, hereby showing the connection between adversarial
perturbation and prediction interpretability.
