Transferability of adversarial examples is critical for black-box deep learning model attacks.
While most existing studies focus on enhancing the transferability of untargeted adversarial …

Deep neural networks are known to be extremely vulnerable to adversarial examples under
white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) …

In recent years, research on adversarial attacks has become a hot spot. Although current
literature on the transfer-based adversarial attack has achieved promising results for …

Abstract Machine learning models are vulnerable to adversarial examples. For the black-box
setting, current substitute attacks need pre-trained models to generate adversarial …

Classic black-box adversarial attacks can take advantage of transferable adversarial
examples generated by a similar substitute model to successfully fool the target model …

It is widely recognized that deep learning models lack robustness to adversarial examples.
An intriguing property of adversarial examples is that they can transfer across different …

An intriguing property of deep neural networks is the existence of adversarial examples,
which can transfer among different architectures. These transferable adversarial examples …

Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool
trained models. Adversarial examples often exhibit black-box transfer, meaning that …

Transferability of adversarial examples is of central importance for attacking an unknown
model, which facilitates adversarial attacks in more practical scenarios, eg, blackbox attacks …

An intriguing property of adversarial examples is their transferability, which suggests that
black-box attacks are feasible in real-world applications. Previous works mostly study the …