Deep learning has evolved 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 …

Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …

Graph deep learning models, such as graph convolutional networks (GCN) achieve
remarkable performance for tasks on graph data. Similar to other types of deep models …

Image compression-based approaches for defending against the adversarial-example
attacks, which threaten the safety use of deep neural networks (DNN), have been …

Advances in machine learning have led to broad deployment of systems with impressive
performance on important problems. Nonetheless, these systems can be induced to make …

Deep neural networks are at the forefront of machine learning research. However, despite
achieving impressive performance on complex tasks, they can be very sensitive: Small …

Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, ie, carefully
perturbed inputs designed to mislead the network at inference time. Recently introduced …

The existence of evasion attacks during the test phase of machine learning algorithms
represents a significant challenge to both their deployment and understanding. These …

While progress has been made in understanding the robustness of machine learning
classifiers to test-time adversaries (evasion attacks), fundamental questions remain …

Despite strong performance in numerous applications, the fragility of deep learning to input
perturbations has raised serious questions about its use in safety-critical domains. While …