A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles. (arXiv:2205.01225v1 [cs.CR])

Adversarial attacks can make deep neural network (DNN) models predict
incorrect output labels, such as misclassified traffic signs, for autonomous
vehicle (AV) perception modules. Resilience against adversarial attacks can
help AVs navigate safely on the road by avoiding misclassication of signs or
objects. This DNN-based study develops a resilient traffic sign classifier for
AVs that uses a hybrid defense method. We use transfer learning to retrain the
Inception-V3 and Resnet-152 models as traffic sign classifiers. This method
also utilizes a …

adversarial attacks autonomous autonomous vehicles defense traffic