Notice

Hit 1444
Subject [CVPR 2022] Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network (by Byung-Kwan Lee and Junho Kim) is accepted in CVPR 2022
Name IVY Lab. KAIST
Date 2022-03-04
Title: Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network
Authors: Byung-Kwan Lee*, Junho Kim*, Yong Man Ro (*: equally contributed)
 
Adversarial examples provoke weak reliability and potential security issues in deep neural networks. Although adversarial training has been widely studied to improve adversarial robustness, it works in an over-parameterized regime and requires high computations and large memory budgets. To bridge adversarial robustness and model compression, we propose a novel adversarial pruning method, Masking Adversarial Damage (MAD) that employs second-order information of adversarial loss function. By using it, we can accurately estimate adversarial saliency for model parameters and determine which parameters can be pruned without weakening adversarial robustness. Furthermore, we reveal that model parameters of initial layer are highly sensitive to the adversarial examples and show that compressed feature representation retains semantic information for the target objects. Through extensive experiments on three public datasets, we demonstrate that MAD effectively prunes adversarially trained networks without loosing adversarial robustness and shows better performance than previous adversarial pruning methods.