Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Despite regularizing the Jacobians of neural networks to enhance model robustness has
directly theoretical correlation with model prediction stability, a large defense performance …

The widespread adoption of Deep Neural Networks (DNNs) in important domains raises
questions about the trustworthiness of DNN outputs. Even a highly accurate DNN will make …

The website fingerprinting (or inter-domain WSF), enhanced by various machine learning
techniques, has shown its power to identify websites a user has visited. To our best …

Thermal infrared detectors have a vast array of potential applications in pedestrian detection
and autonomous driving, and their safety performance is of great concern. Recent works use …

While deep neural networks (DNNs) have made remarkable advancements in various fields
recently, the latest research indicates that DNNs are susceptible to disruptions from minor …

Deep Neural Networks (DNNs) have been widely adopted, yet DNN models are surprisingly
unreliable, which raises significant concerns about their use in critical domains. In this work …

In the physical world, the interplay of light and shadow can significantly impact the
performance of deep neural networks (DNNs), leading to substantial consequences, as …

Nowadays, the susceptibility of deep neural networks (DNNs) has garnered significant
attention. Researchers are exploring patch-based physical attacks, yet traditional …

Adversarial Defense of deep neural networks have gained significant attention and there
have been active research efforts on model blind-points for attacking such as gradient …