标题： 鲁棒性特征适配器用于高效对抗训练 显示英文标题

标题： Robustness Feature Adapter for Efficient Adversarial Training

摘要： 对抗训练（AT）结合投影梯度下降是提高模型在对抗攻击下鲁棒性的最常用方法。 然而，当将AT应用于大型主干模型时，计算开销变得难以承受。 AT还被认为存在鲁棒过拟合的问题。 本文致力于同时解决这两个问题，以构建更值得信赖的基础模型。 具体而言，我们提出了一种新的基于适配器的高效AT方法，直接在特征空间中进行。 我们证明，所提出的基于适配器的方法可以通过消除鲁棒过拟合来改善内循环收敛质量。 结果表明，它显著提高了计算效率，并通过将对抗鲁棒性推广到未见过的攻击来提高了模型精度。 我们在不同的主干架构以及大规模AT中展示了这种新基于适配器的方法的有效性。 显示英文摘要

摘要： Adversarial training (AT) with projected gradient descent is the most popular method to improve model robustness under adversarial attacks. However, computational overheads become prohibitively large when AT is applied to large backbone models. AT is also known to have the issue of robust overfitting. This paper contributes to solving both problems simultaneously towards building more trustworthy foundation models. In particular, we propose a new adapter-based approach for efficient AT directly in the feature space. We show that the proposed adapter-based approach can improve the inner-loop convergence quality by eliminating robust overfitting. As a result, it significantly increases computational efficiency and improves model accuracy by generalizing adversarial robustness to unseen attacks. We demonstrate the effectiveness of the new adapter-based approach in different backbone architectures and in AT at scale.