The score-based query attacks (SQAs) pose practical threats to deep neural networks by
crafting adversarial perturbations within dozens of queries, only using the model's output …

The research on attack transferability is of great importance as it can guide how to conduct
an adversarial attack without knowing any information about target models. However, it …

Most existing adversarial attack methods against detectors involve adding adversarial
perturbations to benign images to synthesize adversarial examples. However, directly …

Deep neural networks (DNNs) have shown vulnerability to adversarial attacks. By exploiting
the transferability of adversarial examples, attackers can fool models under black-box …

Although fast adversarial training provides an efficient approach for building robust
networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where …

Transferable adversarial attack is always in the spotlight since deep learning models have
been demonstrated to be vulnerable to adversarial samples. However, existing physical …

Vision Transformers (ViTs) have achieved great success in UAV target detection tasks.
However, little attention has been paid to the adversarial attack against ViT-based detectors …

Deep neural networks (DNNs) are vulnerable to adversarial attacks, which can cause
security risks in computer information systems. Feature disruption attacks, as a typical form …

The wide application of deep neural networks (DNNs) demands an increasing amount of
attention to their real-world robustness, ie, whether a DNN resists black-box adversarial …

Deep neural networks (DNNs) are acknowledged as vulnerable to adversarial attacks while
the existing black-box attacks require extensive queries on the victim DNN to achieve high …