This work aims to tackle Model Inversion (MI) attack on Split Federated
...
Recent advancements of Deep Neural Networks (DNNs) have seen widespread
...
Neural network stealing attacks have posed grave threats to neural netwo...
Adversarial attacks on Neural Network weights, such as the progressive
b...
The wide deployment of Deep Neural Networks (DNN) in high-performance cl...
Spurred by widening gap between data processing speed and data communica...
Deep Neural Network (DNN) attacks have mostly been conducted through
adv...
Security of machine learning is increasingly becoming a major concern du...
Analog computing based on memristor technology is a promising solution t...
Security of modern Deep Neural Networks (DNNs) is under severe scrutiny ...
Large deep neural network (DNN) models pose the key challenge to energy
...
As deep neural networks (DNNs) have become increasingly important and
po...
Deep Neural Network (DNN) trained by the gradient descent method is know...
Herein, a bit-wise Convolutional Neural Network (CNN) in-memory accelera...
With Von-Neumann computing architectures struggling to address
computati...
Several important security issues of Deep Neural Network (DNN) have been...
Several important security issues of Deep Neural Network (DNN) have been...
Recent development in the field of Deep Learning have exposed the underl...
In the past years, Deep convolution neural network has achieved great su...
Deep convolution neural network has achieved great success in many artif...
Recent studies have shown that deep neural networks (DNNs) are vulnerabl...
The recent success of deep neural networks is powered in part by large-s...
Deep learning algorithms and networks are vulnerable to perturbed inputs...
Deep learning algorithms and networks are vulnerable to perturbed inputs...
In this work, we have proposed a revolutionary neuromorphic computing
me...