In this paper, we use the interaction inside adversarial perturbations to
explain and boost the adversarial transferability. We discover and prove the
negative correlation between the adversarial transferability and the
interaction inside adversarial perturbations. The negative correlation is
further verified through different DNNs with various inputs. Moreover, this
negative correlation can be regarded as a unified perspective to understand
current transferability-boosting methods. To this end, we prove that some
classic methods of enhancing the transferability essentially decease
interactions inside adversarial perturbations. Based on this, we propose to
directly penalize interactions during the attacking process, which
significantly improves the adversarial transferability.

通过对抗扰动内部的交互作用来解释和提高对抗性可传递性，并发现和证明了对抗性可传递性与对抗扰动内部交互作用之间的负相关关系。

统一方法解释与增强对抗性转移

A Unified Approach to Interpreting and Boosting Adversarial  Transferability

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.

本文分析传递学习（knowledge transferability）与对抗可迁移性（adversarial transferability）之间的相互作用，并提出两种实用的对抗可迁移性度量，通过广泛的实验证明了对抗可迁移性与知识可迁移性之间的正相关性，为未来有效的知识转移学习和对抗迁移性分析提供了启示。