  Rapid advancements of deep learning are accelerating adoption in a wide
variety of applications, including safety-critical applications such as
self-driving vehicles, drones, robots, and surveillance systems. These
advancements include applying variations of sophisticated techniques that
improve the performance of models. However, such models are not immune to
adversarial manipulations, which can cause the system to misbehave and remain
unnoticed by experts. The frequency of modifications to existing deep learning
models necessitates thorough analysis to determine the impact on models'
robustness. In this work, we present an experimental evaluation of the effects
of model modifications on deep learning model robustness using adversarial
attacks. Our methodology involves examining the robustness of variations of
models against various adversarial attacks. By conducting our experiments, we
aim to shed light on the critical issue of maintaining the reliability and
safety of deep learning models in safety- and security-critical applications.
Our results indicate the pressing demand for an in-depth assessment of the
effects of model changes on the robustness of models.
