Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

As a powerful modelling method, piecewise linear neural networks (PWLNNs) have proven
successful in various fields, most recently in deep learning. To apply PWLNN methods, both …

For black-box attacks, the gap between the substitute model and the victim model is usually
large, which manifests as a weak attack performance. Motivated by the observation that the …

Many existing adversarial attacks generate-norm perturbations on image RGB space.
Despite some achievements in transferability and attack success rate, the crafted adversarial …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

The black-box adversarial attack has attracted impressive attention for its practical use in the
field of deep learning security. Meanwhile, it is very challenging as there is no access to the …

In this work, we propose the first backdoor attack to graph neural networks (GNN).
Specifically, we propose a subgraph based backdoor attack to GNN for graph classification …

In a data poisoning attack, an attacker modifies, deletes, and/or inserts some training
examples to corrupt the learnt machine learning model. Bootstrap Aggregating (bagging) is …

While the transferability property of adversarial examples allows the adversary to perform
black-box attacks ie, the attacker has no knowledge about the target model), the transfer …