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

During the last decade, Deep Neural Networks (DNN) have progressively been integrated
on all types of platforms, from data centers to embedded systems including low-power …

With the recent advancements in machine learning theory, many commercial embedded
microprocessors use neural network (NN) models for a variety of signal processing …

Recent advances in neural networks (NNs) and their applications in deep learning
techniques have made the security aspects of NNs an important and timely topic for …

Recent work on stealing machine learning (ML) models from inference engines with
physical side-channel attacks warrant an urgent need for effective side-channel defenses …

With the recent advancements in machine learning (ML) theory, a lot of energy-efficient
neural network (NN) accelerators have been developed. However, their associated side …

Machine learning (ML) models can be trade secrets due to their development cost. Hence,
they need protection against malicious forms of reverse engineering (eg, in IP piracy). With a …

Embedded systems for edge computing are getting more powerful, and some are equipped
with a GPU to enable on-device deep neural network (DNN) learning tasks such as image …

Trained Deep Neural Network (DNN) models have become valuable intellectual property. A
new attack surface has emerged for DNNs: model reverse engineering. Several recent …

The advancement of deep neural networks (DNNs) motivates the deployment in various
domains, including image classification, disease diagnoses, voice recognition, etc. Since …