  Deep neural networks (DNNs) are vulnerable to adversarial examples that are
carefully designed to cause the deep learning model to make mistakes.
Adversarial examples of 2D images and 3D point clouds have been extensively
studied, but studies on event-based data are limited. Event-based data can be
an alternative to a 2D image under high-speed movements, such as autonomous
driving. However, the given adversarial events make the current deep learning
model vulnerable to safety issues. In this work, we generate adversarial
examples and then train the robust models for event-based data, for the first
time. Our algorithm shifts the time of the original events and generates
additional adversarial events. Additional adversarial events are generated in
two stages. First, null events are added to the event-based data to generate
additional adversarial events. The perturbation size can be controlled with the
number of null events. Second, the location and time of additional adversarial
events are set to mislead DNNs in a gradient-based attack. Our algorithm
achieves an attack success rate of 97.95\% on the N-Caltech101 dataset.
Furthermore, the adversarial training model improves robustness on the
adversarial event data compared to the original model.
