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

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

To date the quest to rapidly and effectively produce human-object interaction (HOI)
animations directly from textual descriptions stands at the forefront of computer vision …

Video, as a key driver in the global explosion of digital information, can create tremendous
benefits for human society. Governments and enterprises are deploying innumerable …

Billions of people are sharing their daily live images on social media everyday. However,
malicious collectors use deep face recognition systems to easily steal their biometric …

Neural style transfer (NST) generates new images by combining the style of one image with
the content of another. However, unauthorized NST can exploit artwork, raising concerns …

Deep learning-based face recognition models are vulnerable to adversarial attacks. In
contrast to general noises, the presence of imperceptible adversarial noises can lead to …

Modern automated surveillance techniques are heavily reliant on deep learning methods.
Despite the superior performance, these learning systems are inherently vulnerable to …

Face Recognition (FR) systems can be easily deceived by adversarial examples that
manipulate benign face images through imperceptible perturbations. Adversarial attacks on …

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial
examples, raising broad security concerns about their applications. Besides the attacks in …