Malware poses a severe threat to cyber security. Attackers use malware to achieve their
malicious purposes, such as unauthorized access, stealing confidential data, blackmailing …

Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies
a progressive section in deep learning. Using generative modeling, the underlying …

Previous work on adversarial examples typically involves a fixed norm perturbation budget,
which fails to capture the way humans perceive perturbations. Recent work has shifted …

In the scenario of black-box adversarial attack, the target model's parameters are unknown,
and the attacker aims to find a successful adversarial perturbation based on query feedback …

Deep neural networks (DNNs), regardless of their impressive performance, are vulnerable to
attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the decision …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by
adding imperceptible perturbations to inputs. Recently different attacks and strategies have …

Deep learning models are widely used in daily life, which bring great convenience to our
lives, but they are vulnerable to attacks. How to build an attack system with strong …

Deep learning models have shown their advantages in computer vision, eg, image
classification, whereas they are well-known to be susceptible to imperceptible perturbations …

Phishing is one of the most popular cyberspace attacks. Phishing detection has been
integrated into mainstream browsers to provide online protection. The phishing detector of …