Deep learning has emerged as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Deep learning (DL) models are inherently vulnerable to adversarial examples–maliciously
crafted inputs to trigger target DL models to misbehave–which significantly hinders the …

Recently, adversarial attacks can be applied to the physical world, causing practical issues
to various Convolutional Neural Networks (CNNs) powered applications. Most existing …

In recent years, neural networks have become the default choice for image classification and
many other learning tasks, even though they are vulnerable to so-called adversarial attacks …

Deep neural networks (DNN) have been shown to be useful in a wide range of applications.
However, they are also known to be vulnerable to adversarial samples. By transforming a …

Deep Learning (DL) is being applied in various domains, especially in safety-critical
applications such as autonomous driving. Consequently, it is of great significance to ensure …

Recently, deep neural networks (DNNs) have been deployed in safety-critical systems such
as autonomous vehicles and medical devices. Shortly after that, the vulnerability of DNNs …

Deep convolutional neural networks (CNNs) can be easily tricked to give incorrect outputs
by adding tiny perturbations to the input that are imperceptible to humans. This makes them …

With a constant improvement in the network architectures and training methodologies,
Neural Networks (NNs) are increasingly being deployed in real-world Machine Learning …

The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed
computer vision. But, the fact that we do not know when a specific DNN will work and when it …