Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to
adversarial examples, resulting from small-magnitude perturbations added to the input …

Deep neural network-based classifiers are known to be vulnerable to adversarial examples
that can fool them into misclassifying their input through the addition of small-magnitude …

Abstract Although Deep neural networks (DNNs) are being pervasively used in vision-based
autonomous driving systems, they are found vulnerable to adversarial attacks where small …

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

Deep learning technology has become an important branch of artificial intelligence.
However, researchers found that deep neural networks, as the core algorithm of deep …

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

Recent work has documented the susceptibility of deep learning systems to adversarial
examples, but most such attacks directly manipulate the digital input to a classifier. Although …

Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs)
based vision systems eg, for classification, segmentation and object detection. The …

Deep neural networks are vulnerable to adversarial examples, which becomes one of the
most important research problems in the development of deep learning. While a lot of efforts …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing
works have mostly focused on either digital adversarial examples created via small and …