Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

Adversarial training (AT) is always formulated as a minimax problem, of which the
performance depends on the inner optimization that involves the generation of adversarial …

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in
securing face recognition systems from presentation attacks (PAs). As more and more …

While deep face recognition (FR) systems have shown amazing performance in
identification and verification, they also arouse privacy concerns for their excessive …

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

Various facial manipulation techniques have drawn serious public concerns in morality,
security, and privacy. Although existing face forgery classifiers achieve promising …

A hard challenge in develo** practical face recognition (FR) attacks is due to the black-
box nature of the target FR model, ie, inaccessible gradient and parameter information to …

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 …

The success of deep learning based face recognition systems has given rise to serious
privacy concerns due to their ability to enable unauthorized tracking of users in the digital …