  Recent advancements in quantum computing have led to the development of
hybrid quantum neural networks (HQNNs) that employ a mixed set of quantum
layers and classical layers, such as Quanvolutional Neural Networks (QuNNs).
While several works have shown security threats of classical neural networks,
such as adversarial attacks, their impact on QuNNs is still relatively
unexplored. This work tackles this problem by designing AdvQuNN, a specialized
methodology to investigate the robustness of HQNNs like QuNNs against
adversarial attacks. It employs different types of Ansatzes as parametrized
quantum circuits and different types of adversarial attacks. This study aims to
rigorously assess the influence of quantum circuit architecture on the
resilience of QuNN models, which opens up new pathways for enhancing the
robustness of QuNNs and advancing the field of quantum cybersecurity. Our
results show that, compared to classical convolutional networks, QuNNs achieve
up to 60\% higher robustness for the MNIST and 40\% for FMNIST datasets.
