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 …

Machine learning (ML) has been widely adopted in various privacy-critical applications, eg,
face recognition and medical image analysis. However, recent research has shown that ML …

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 …

Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial
examples, there is an urgent need for an effective adversarial attack to identify the …

The advent of the Internet of Things (IoT) has brought forth an era of unprecedented
connectivity, with an estimated 80 billion smart devices expected to be in operation by the …

Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible
adversarial perturbations. As a complementary type of adversary, patch attacks that …

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks,
yet they are under the risk of adversarial examples that can be easily generated when the …

Deep learning models have shown their vulnerability when dealing with adversarial attacks.
Existing attacks almost perform on low-level instances, such as pixels and super-pixels, and …

Abstract Machine learning classifiers are critically prone to evasion attacks. Adversarial
examples are slightly modified inputs that are then misclassified, while remaining …

Decision-based attack poses a severe threat to real-world applications since it regards the
target model as a black box and only accesses the hard prediction label. Great efforts have …