# Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability

arXiv: https://arxiv.org/abs/2006.14512 · HTML: https://mlsanity.com/paper/2006.14512/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 22 of 22)

- [Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training](https://mlsanity.com/paper/2102.04716/) (2021)
- [A Survey on Transferability of Adversarial Examples across Deep Neural Networks](https://mlsanity.com/paper/2310.17626/) (2023)
- [Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability](https://mlsanity.com/paper/2307.07873/) (2023)
- [A Survey on Out-of-Distribution Evaluation of Neural NLP Models](https://mlsanity.com/paper/2306.15261/) (2023)
- [Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack](https://mlsanity.com/paper/2411.02669/) (2024)
- [Towards Good Practices in Evaluating Transfer Adversarial Attacks](https://mlsanity.com/paper/2211.09565/) (2022)
- [Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger](https://mlsanity.com/paper/2412.07277/) (2024)
- [The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence](https://mlsanity.com/paper/2208.01545/) (2022)
- [Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization](https://mlsanity.com/paper/2202.01832/) (2022)
- [Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization](https://mlsanity.com/paper/2403.11397/) (2024)
- [Is Pre-training Truly Better Than Meta-Learning?](https://mlsanity.com/paper/2306.13841/) (2023)
- [SoK: Pitfalls in Evaluating Black-Box Attacks](https://mlsanity.com/paper/2310.17534/) (2023)
- [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://mlsanity.com/paper/2311.11796/) (2023)
- [Exploring Adversarial Robustness of Deep State Space Models](https://mlsanity.com/paper/2406.05532/) (2024)
- [Publishing Efficient On-device Models Increases Adversarial Vulnerability](https://mlsanity.com/paper/2212.13700/) (2022)
- [RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses](https://mlsanity.com/paper/2412.20987/) (2024)
- [UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning](https://mlsanity.com/paper/2211.09717/) (2022)
- [Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessment](https://mlsanity.com/paper/2505.03519/) (2025)
- [Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed](https://mlsanity.com/paper/2510.01494/) (2025)
- [The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence](https://mlsanity.com/paper/2112.13121/) (2021)
- [Learning Optimal Prompt Ensemble for Multi-source Visual Prompt Transfer](https://mlsanity.com/paper/2504.12311/) (2025)
- [Visual-Invariance-Augmented Feature Optimal Alignment for Transferable Adversarial Attacks against Closed-Source MLLMs](https://mlsanity.com/paper/2610.06977/) (2026)

## Abstract (arXiv)

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, 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.