Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

Universal adversarial perturbation (UAP), also known as image-agnostic perturbation, is a
fixed perturbation map that can fool the classifier with high probabilities on arbitrary images …

Deep neural networks (DNNs) are vulnerable to highly transferable adversarial attacks.
Especially many studies have shown that sparse attacks pose a significant threat to DNNs …

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

Adversarial attacks on Face Recognition (FR) systems have proven highly effective in
compromising pure FR models, yet adversarial examples may be ineffective to the complete …

Vision transformers (ViTs) perform exceptionally well in various computer vision tasks but
remain vulnerable to adversarial attacks. Recent studies have shown that the transferability …

With the great development of generative model techniques, face forgery detection draws
more and more attention in the related field. Researchers find that existing face forgery …

Near infrared-visible (NIR-VIS) heterogeneous face recognition aims to match face identities
in cross-modality settings, which has achieved significant development recently. The work …

Face Recognition (FR) models are vulnerable to adversarial examples that subtly
manipulate benign face images, underscoring the urgent need to improve the transferability …