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

Adversarial attacks have been extensively studied in recent years since they can identify the
vulnerability of deep learning models before deployed. In this paper, we consider the black …

In the scenario of black-box adversarial attack, the target model's parameters are unknown,
and the attacker aims to find a successful adversarial perturbation based on query feedback …

This work studies black-box adversarial attacks against deep neural networks (DNNs),
where the attacker can only access the query feedback returned by the attacked DNN …

We present a new method for black-box adversarial attack. Unlike previous methods that
combined transfer-based and scored-based methods by using the gradient or initialization of …

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

The black-box adversarial attack has attracted impressive attention for its practical use in the
field of deep learning security. Meanwhile, it is very challenging as there is no access to the …

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 (DNNs) have demonstrated excellent performance on various tasks,
however they are under the risk of adversarial examples that can be easily generated when …

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 …