The robustness of deep learning models against adversarial attacks remains a pivotal concern. This study presents, for the first time, an exhaustive review of the transferability aspect of adversarial attacks. It systematically categorizes and critically evaluates various methodologies developed to augment the transferability of adversarial attacks. This study encompasses a spectrum of techniques, including Generative Structure, Semantic Similarity, Gradient Editing, Target Modification, and Ensemble Approach. Concurrently, this paper introduces a benchmark framework \textit{TAA-Bench}, integrating ten leading methodologies for adversarial attack transferability, thereby providing a standardized and systematic platform for comparative analysis across diverse model architectures. Through comprehensive scrutiny, we delineate the efficacy and constraints of each method, shedding light on their underlying operational principles and practical utility. This review endeavors to be a quintessential resource for both scholars and practitioners in the field, charting the complex terrain of adversarial transferability and setting a foundation for future explorations in this vital sector. The associated codebase is accessible at: https://github.com/KxPlaug/TAA-Bench

翻译：深度学习模型对对抗攻击的鲁棒性仍是关键问题。本研究首次全面综述了对抗攻击的可迁移性方面，系统分类并批判性评估了旨在增强对抗攻击可迁移性的各类方法。研究涵盖多种技术体系，包括生成结构、语义相似性、梯度编辑、目标修改及集成方法。同时，本文提出基准框架\textit{TAA-Bench}，集成十种对抗攻击可迁移性前沿方法，为跨多种模型架构的对比分析提供标准化系统平台。通过全面剖析，我们阐明每种方法的效能与局限，揭示其底层运行原理与实际应用价值。本综述旨在成为该领域学者与从业者的经典参考资源，勾勒对抗可迁移性的复杂图景，并为这一关键领域的未来探索奠定基础。相关代码库可访问：https://github.com/KxPlaug/TAA-Bench