Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries. (arXiv:2208.08697v1 [cs.LG])

The security of deep learning (DL) systems is an extremely important field of
study as they are being deployed in several applications due to their
ever-improving performance to solve challenging tasks. Despite overwhelming
promises, the deep learning systems are vulnerable to crafted adversarial
examples, which may be imperceptible to the human eye, but can lead the model
to misclassify. Protections against adversarial perturbations on ensemble-based
techniques have either been shown to be vulnerable to stronger adversaries or
shown to lack …

adversarial attacks decision lg networks neural networks