Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable
Diffusion, less research attention is paid to its adversarial robustness. In this work, we study …

Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many
signal processing and machine learning (ML) applications. It is used for solving optimization …

The transferability of adversarial examples allows the deception on black-box models, and
transfer-based targeted attacks have attracted a lot of interest due to their practical …

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

Deep neural networks are vulnerable to adversarial attacks. Among different attack settings,
the most challenging yet the most practical one is the hard-label setting where the attacker …

With the significant development of large models in recent years, Large Vision-Language
Models (LVLMs) have demonstrated remarkable capabilities across a wide range of …

Current transfer learning methods are mainly based on finetuning a pretrained model with
target-domain data. Motivated by the techniques from adversarial machine learning (ML) …