Adversary ML Resilience in Autonomous Driving Through Human Centered
  Perception Mechanisms
Aakriti Shah
Physical adversarial attacks on road signs are continuously exploiting
vulnerabilities in modern day autonomous vehicles (AVs) and impeding their
ability to correctly classify what type of road sign they encounter. Current
models cannot generalize input data well, resulting in overfitting or
underfitting. In overfitting, the model memorizes the input data but cannot
generalize to new scenarios. In underfitting, the model does not learn enough
of the input data to accurately classify these road signs. This paper explores
the resilience of autonomous driving systems against three main physical
adversarial attacks (tape, graffiti, illumination), specifically targeting
object classifiers. Several machine learning models were developed and
evaluated on two distinct datasets: road signs (stop signs, speed limit signs,
traffic lights, and pedestrian crosswalk signs) and geometric shapes (octagons,
circles, squares, and triangles). The study compared algorithm performance
under different conditions, including clean and adversarial training and
testing on these datasets. To build robustness against attacks, defense
techniques like adversarial training and transfer learning were implemented.
Results demonstrated transfer learning models played a crucial role in
performance by allowing knowledge gained from shape training to improve
generalizability of road sign classification, despite the datasets being
completely different. The paper suggests future research directions, including
human-in-the-loop validation, security analysis, real-world testing, and
explainable AI for transparency. This study aims to contribute to improving
security and robustness of object classifiers in autonomous vehicles and
mitigating adversarial example impacts on driving systems.