Autonomous driving systems critically depend on accurately classifying traffic signs, a task
that is jeopardized by adversarial attacks. This paper focuses on the relatively unexplored …

Deep neural networks have been used extensively for diverse visual tasks, including object
detection, face recognition, and image classification. However, they face several security …

Deep learning models are highly vulnerable to adversarial examples, leading to significant
attention on techniques for detecting them. However, current methods primarily rely on …

Abstract Diffusion Models (DMs) are probabilistic models that create realistic samples by
simulating the diffusion process, gradually adding and removing noise from data. These …

In recent academic discussions surrounding the textual domain, there has been significant
attention directed towards adversarial examples. Despite this focus, the area of detecting …

Black-box query attacks, which rely only on the output of the victim model, have proven to be
effective in attacking deep learning models. However, existing black-box query attacks show …