As “deep neural networks”(DNNs) achieve increasing accuracy, they are getting employed
in increasingly diverse applications, including security-critical applications such as medical …

With the increasing proliferation of hardware accelerators and the predicted continued
increase in the heterogeneity of future computing systems, it is necessary to understand the …

The deep learning revolution has been enabled in large part by GPUs, and more recently
accelerators, which make it possible to carry out computationally demanding training and …

Recent studies show that on-device deployed deep learning (DL) models, such as those of
Tensor Flow Lite (TFLite), can be easily extracted from real-world applications and devices …

Neural network stealing attacks have posed grave threats to neural network model
deployment. Such attacks can be launched by extracting neural architecture information …

Deploying deep learning (DL) models on mobile applications (Apps) has become ever-more
popular. However, existing studies show attackers can easily reverse-engineer mobile DL …

Deep neural network (DNN) intellectual property (IP) models must be kept undisclosed to
avoid revealing trade secrets. Recent works have devised machine learning techniques that …

Virtualization technology makes cloud resources affordable. Virtual Machines (VMs) are
widely used on top of host machines in cloud computing environments. Adversaries target …

Deep Neural Networks (DNNs) have become ubiquitous due to their performance on
prediction and classification problems. However, they face a variety of threats as their usage …

Deep Neural Networks (DNNs) have become invaluable intellectual property for AI providers
due to advancements fueled by a decade of research and development. However, recent …