MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization

1 code implementation • 20 Jun 2024 • Zhaozhe Hu, Jia-Li Yin, Bin Chen, Luojun Lin, Bo-Hao Chen, Ximeng Liu

Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA).

An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability

1 code implementation • ICCV 2023 • Bin Chen, Jia-Li Yin, Shukai Chen, Bo-Hao Chen, Ximeng Liu

Alternatively, model ensemble adversarial attacks are proposed to fuse outputs from surrogate models with diverse architectures to get an ensemble loss, making the generated adversarial example more likely to transfer to other models as it can fool multiple models concurrently.

Adversarial Attack

SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

1 code implementation • 12 Dec 2022 • Wanqing Zhu, Jia-Li Yin, Bo-Hao Chen, Ximeng Liu

In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models.

Adversarial Robustness Unsupervised Domain Adaptation

Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness

no code implementations • 1 Dec 2021 • Jia-Li Yin, Lehui Xie, Wanqing Zhu, Ximeng Liu, Bo-Hao Chen

However, most of the existing adversarial training methods focus on improving the robust accuracy by strengthening the adversarial examples but neglecting the increasing shift between natural data and adversarial examples, leading to a dramatic decrease in natural accuracy.

Adversarial Robustness