  Deep neural networks (DNNs) have accomplished impressive success in various
applications, including autonomous driving perception tasks, in recent years.
On the other hand, current deep neural networks are easily fooled by
adversarial attacks. This vulnerability raises significant concerns,
particularly in safety-critical applications. As a result, research into
attacking and defending DNNs has gained much coverage. In this work, detailed
adversarial attacks are applied on a diverse multi-task visual perception deep
network across distance estimation, semantic segmentation, motion detection,
and object detection. The experiments consider both white and black box attacks
for targeted and un-targeted cases, while attacking a task and inspecting the
effect on all the others, in addition to inspecting the effect of applying a
simple defense method. We conclude this paper by comparing and discussing the
experimental results, proposing insights and future work. The visualizations of
the attacks are available at https://youtu.be/6AixN90budY.
