Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning

no code implementations • 15 Oct 2023 • Yulong Yang, Chenhao Lin, Xiang Ji, Qiwei Tian, Qian Li, Hongshan Yang, Zhibo Wang, Chao Shen

Instead, a one-shot adversarial augmentation prior to training is sufficient, and we name this new defense paradigm Data-centric Robust Learning (DRL).

Fairness

Hard Adversarial Example Mining for Improving Robust Fairness

no code implementations • 3 Aug 2023 • Chenhao Lin, Xiang Ji, Yulong Yang, Qian Li, Chao Shen, Run Wang, Liming Fang

Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE).

Fairness