论文标题
发现对抗性可传递性与知识转移性之间的联系
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability
论文作者
论文摘要
知识转移性或转移学习已被广泛采用，以使源域中的预训练模型有效地适应目标域中的下游任务。因此，重要的是要探索和理解影响知识传递性的因素。在本文中，作为第一项工作，我们分析并演示了知识传递性与另一个重要现象之间的联系 - 对抗性转移性，\ emph {i.e。}，可以将针对一个模型产生的对抗性示例转移到攻击其他模型中。我们的理论研究表明，对抗性可传递性表明知识的转移性，反之亦然。此外，基于理论见解，我们提出了两个实用的对抗可传递性指标来表征此过程，并用作对抗性和知识传递性之间的双向指标。我们对各种数据集的不同方案进行了广泛的实验，显示了对抗性可传递性和知识转移性之间的正相关。我们的发现将阐明有关有效知识转移学习和对抗性转移性分析的未来研究。
Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the target domain. It is thus important to explore and understand the factors affecting knowledge transferability. In this paper, as the first work, we analyze and demonstrate the connections between knowledge transferability and another important phenomenon--adversarial transferability, \emph{i.e.}, adversarial examples generated against one model can be transferred to attack other models. Our theoretical studies show that adversarial transferability indicates knowledge transferability and vice versa. Moreover, based on the theoretical insights, we propose two practical adversarial transferability metrics to characterize this process, serving as bidirectional indicators between adversarial and knowledge transferability. We conduct extensive experiments for different scenarios on diverse datasets, showing a positive correlation between adversarial transferability and knowledge transferability. Our findings will shed light on future research about effective knowledge transfer learning and adversarial transferability analyses.
