  The emergence of Deep Neural Networks (DNNs) has revolutionized various
domains by enabling the resolution of complex tasks spanning image recognition,
natural language processing, and scientific problem-solving. However, this
progress has also brought to light 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 which circumvents 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
opportunities are discussed, highlighting the importance of fortifying DNNs
against adversarial vulnerabilities in an evolving landscape.
