Recent research has found that many families of machine learning models are vulnerable to
adversarial examples: inputs that are specifically designed to cause the target model to …

Adversarial training, in which a network is trained on adversarial examples, is one of the few
defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high …

Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers
by adding imperceptible perturbations. An intriguing property of adversarial examples is …

Deep learning models are vulnerable to adversarial examples crafted by applying human-
imperceptible perturbations on benign inputs. However, under the black-box setting, most …

Though CNNs have achieved the state-of-the-art performance on various vision tasks, they
are vulnerable to adversarial examples---crafted by adding human-imperceptible …

Adversarial examples that fool machine learning models, particularly deep neural networks,
have been a topic of intense research interest, with attacks and defenses being developed …

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 …

Research on adversarial examples in computer vision tasks has shown that small, often
imperceptible changes to an image can induce misclassification, which has security …

We introduce a feature scattering-based adversarial training approach for improving model
robustness against adversarial attacks. Conventional adversarial training approaches …

Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by
perturbing correctly classified inputs to cause DNN models to misbehave (eg …