A Survey on Transferability of Adversarial Examples across Deep Neural
  Networks
Jindong Gu Xiaojun Jia Pau de Jorge Wenqain Yu Xinwei Liu Avery Ma Yuan Xun Anjun Hu Ashkan Khakzar Zhijiang Li Xiaochun Cao Philip Torr
The emergence of Deep Neural Networks (DNNs) has revolutionized various
domains, enabling the resolution of complex tasks spanning image recognition,
natural language processing, and scientific problem-solving. However, this
progress has also exposed a concerning vulnerability: adversarial examples.
These crafted inputs, imperceptible to humans, can manipulate machine learning
models into making erroneous predictions, raising concerns for safety-critical
applications. An intriguing property of this phenomenon is the transferability
of adversarial examples, where perturbations crafted for one model can deceive
another, often with a different architecture. This intriguing property enables
"black-box" attacks, circumventing the need for detailed knowledge of the
target model. This survey explores the landscape of the adversarial
transferability of adversarial examples. We categorize existing methodologies
to enhance adversarial transferability and discuss the fundamental principles
guiding each approach. While the predominant body of research primarily
concentrates on image classification, we also extend our discussion to
encompass other vision tasks and beyond. Challenges and future prospects are
discussed, highlighting the importance of fortifying DNNs against adversarial
vulnerabilities in an evolving landscape.