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-BenchComment: Accepted by NDSS 2024 Worksho