保持鲁棒与保持公平：对齐公平性与鲁棒性
To be Robust and to be Fair: Aligning Fairness with Robustness
AI总结：
本文研究了对抗训练与攻击中公平性与准确率的关系，提出统一公平性攻击结构并证明其与准确率攻击的对齐性，实验表明该方法能同时提升两种指标的鲁棒性。
摘要：
对抗训练已被证明在提升针对对抗样本的鲁棒性方面是可靠的。然而，对抗训练在公平性方面的问题尚未得到妥善研究，公平性与准确率攻击之间的关系仍不清晰。我们能否同时提升关于公平性和准确率的鲁棒性？为探讨此问题，本文研究了关于这两种指标的对抗训练与对抗攻击问题。我们为公平性攻击提出了一种统一结构，汇集了群体公平性中的常见概念，并从理论上证明了针对不同概念的公平性攻击的等价性。此外，我们展示了公平性与准确率攻击的对齐关系，并从理论上证明了关于一种指标的鲁棒性得益于关于另一种指标的鲁棒性。本研究提出了一种统一关于公平性和准确率的对抗训练与攻击的新方法，实验结果表明，我们提出的方法在关于这两种指标的鲁棒性方面取得了更好的性能。
英文摘要：
Adversarial training has been shown to be reliable in improving robustness against adversarial samples. However, the problem of adversarial training in terms of fairness has not yet been properly studied, and the relationship between fairness and accuracy attack still remains unclear. Can we simultaneously improve robustness w.r.t. both fairness and accuracy? To tackle this topic, in this paper, we study the problem of adversarial training and adversarial attack w.r.t. both metrics. We propose a unified structure for fairness attack which brings together common notions in group fairness, and we theoretically prove the equivalence of fairness attack against different notions. Moreover, we show the alignment of fairness and accuracy attack, and theoretically demonstrate that robustness w.r.t. one metric benefits from robustness w.r.t. the other metric. Our study suggests a novel way to unify adversarial training and attack w.r.t. fairness and accuracy, and experimental results show that our proposed method achieves better performance in terms of robustness w.r.t. both metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
