  Image classification must work for autonomous vehicles (AV) operating on
public roads, and actions performed based on image misclassification can have
serious consequences. Traffic sign images can be misclassified by an
adversarial attack on machine learning models used by AVs for traffic sign
recognition. To make classification models resilient against adversarial
attacks, we used a hybrid deep-learning model with both the quantum and
classical layers. Our goal is to study the hybrid deep-learning architecture
for classical-quantum transfer learning models to support the current era of
intermediate-scale quantum technology. We have evaluated the impacts of various
white box adversarial attacks on these hybrid models. The classical part of
hybrid models includes a convolution network from the pre-trained Resnet18
model, which extracts informative features from a high dimensional LISA traffic
sign image dataset. The output from the classical processor is processed
further through the quantum layer, which is composed of various quantum gates
and provides support to various quantum mechanical features like entanglement
and superposition. We have tested multiple combinations of quantum circuits to
provide better classification accuracy with decreasing training data and found
better resiliency for our hybrid classical-quantum deep learning model during
attacks compared to the classical-only machine learning models.
