Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …

Abstract Vision-Language Pre-training (VLP) models have achieved remarkable success in
practice, while easily being misled by adversarial attack. Though harmful, adversarial …

Adversarial attacks, particularly the Fast Gradient Sign Method (FGSM) and Projected
Gradient Descent (PGD) pose significant threats to the robustness of deep learning models …

Large Vision-Language Models (LVLMs) have demonstrated their powerful multimodal
capabilities. However, they also face serious safety problems, as adversaries can induce …

There has been a surge of interest in using machine learning (ML) to automatically detect
malware through their dynamic behaviors. These approaches have achieved significant …

Deep neural networks (DNNs) are vulnerable to the adversarial attack which is maliciously
implemented by adding human-imperceptible perturbation to images and thus leads to …

Defenses against adversarial attacks are essential to ensure the reliability of machine-
learning models as their applications are expanding in different domains. Existing ML …

Various adversarial attack methods pose a threat to secure machine learning models. Pre-
processing-based defense against adversarial input was not adequate, and they are …

Several recent studies have shown that artificial intelligence (AI) systems can malfunction
due to intentionally manipulated data coming through normal channels. Such kinds of …

Detection-based defense approaches are effective against adversarial attacks without
compromising the structure of the protected model. However, they could be bypassed by …