Deep neural networks (DNNs) are known to be sensitive to adversarial input
perturbations, leading to a reduction in either prediction accuracy or
individual fairness. To jointly characterize the susceptibility of prediction
accuracy and individual fairness to adversarial perturbations, we introduce a
novel robustness definition termed robust accurate fairness. Informally, robust
accurate fairness requires that predictions for an instance and its similar
counterparts consistently align with the ground truth when subjected to input
perturbations. We propose an adversarial attack approach dubbed RAFair to
expose false or biased adversarial defects in DNN, which either deceive
accuracy or compromise individual fairness. Then, we show that such adversarial
instances can be effectively addressed by carefully designed benign
perturbations, correcting their predictions to be accurate and fair. Our work
explores the double-edged sword of input perturbations to robust accurate
fairness in DNN and the potential of using benign perturbations to correct
adversarial instances.

深度神经网络对于对抗性输入扰动具有敏感性，为了同时表征预测准确性和个体公平性对于对抗性扰动的易感性，我们引入了一个新的鲁棒性定义，称为鲁棒准确公平性。我们提出了一种名为 RAFair 的对抗性攻击方法，用以暴露在深度神经网络中存在的虚假或有偏见的对抗缺陷，可以通过精心设计的良性扰动来纠正这些对抗实例，从而使其预测准确且公平。我们的工作探索了对输入扰动在深度神经网络的鲁棒准确公平性上的双刃剑作用，并展示了利用良性扰动来纠正对抗实例的潜力。