MAD: Meta Adversarial Defense Benchmark

no code implementations • 18 Sep 2023 • X. Peng, D. Zhou, G. Sun, J. Shi, L. Wu

In addition, we introduce a meta-learning based adversarial training (Meta-AT) algorithm as the baseline, which features high robustness to unseen adversarial attacks through few-shot learning.

Adversarial Defense Few-Shot Learning