2019
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness
CVPR 2019poster
Defending adversarial attack is a critical step towards reliable deployment of deep learning empowered solutions for industrial applications. Probabilistic adversarial robustness (PAR), as a theoretical framework, is introduced to neutralize adversarial attacks by concentrating sample probability to…