Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …

The emergence of Deep Neural Networks (DNNs) has revolutionized various domains,
enabling the resolution of complex tasks spanning image recognition, natural language …

The transferability of adversarial examples across deep neural networks (DNNs) is the crux
of many black-box attacks. Many prior efforts have been devoted to improving the …

The adversarial vulnerability of deep neural networks (DNNs) has drawn great attention due
to the security risk of applying these models in real-world applications. Based on …

Adversarial examples for deep neural networks (DNNs) are transferable: examples that
successfully fool one white-box surrogate model can also deceive other black-box models …

Vision-language pre-training (VLP) models demonstrate impressive abilities in processing
both images and text. However, they are vulnerable to multi-modal adversarial examples …

Neural networks are susceptible to adversarial perturbations that are transferable across
different models. In this paper, we introduce a novel model alignment technique aimed at …

Diabetic Retinopathy (DR) is a leading cause of vision loss around the world. To help
diagnose it, numerous cutting-edge works have built powerful deep neural networks (DNNs) …

Adversarial examples (AEs) with small adversarial perturbations can mislead deep neural
networks (DNNs) into wrong predictions. The AEs created on one DNN can also fool another …

In the transfer-based adversarial attacks, adversarial examples are only generated by the
surrogate models and achieve effective perturbation in the victim models. Although …