5 papers
RegMix: Adversarial Mutual and Generalization Regularization for Enhancing DNN Robustness
Zhenyu Liu, Varun Ojha
Adversarial training is the most effective defense against adversarial attacks. The effectiveness of the adversarial attacks has been on the design of its loss function and regular…
AdaGAT: Adaptive Guidance Adversarial Training for the Robustness of Deep Neural Networks
Zhenyu Liu, Huizhi Liang, Xinrun Li +2
Adversarial distillation (AD) is a knowledge distillation technique that facilitates the transfer of robustness from teacher deep neural network (DNN) models to lightweight target…
D2R: dual regularization loss with collaborative adversarial generation for model robustness
Zhenyu Liu, Huizhi Liang, Rajiv Ranjan +3
The robustness of Deep Neural Network models is crucial for defending models against adversarial attacks. Recent defense methods have employed collaborative learning frameworks to…
TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions
Wang YuHang, Junkang Guo, Aolei Liu +5
Adversarial robustness is a critical challenge in deploying deep neural networks for real-world applications. While adversarial training is a widely recognized defense strategy, mo…
Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
Zhenyu Liu, Haoran Duan, Huizhi Liang +5
Adversarial training is one of the most effective methods for enhancing model robustness. Recent approaches incorporate adversarial distillation in adversarial training architectur…