Face recognition systems are susceptible to adversarial attacks, where adversarial facial
images are generated without awareness of the intrinsic attributes of the images in existing …

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and
vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate …

Recent research has shown the vulnerability of deep networks to adversarial perturbations.
Adversarial training and its variants have been shown to be effective defense algorithms …

Adversarial patches, a type of adversarial example, pose serious security threats to deep
neural networks (DNNs) by inducing erroneous outputs. Existing gradient stabilization …

In this paper, we propose an advanced method for adversarial training that focuses on
leveraging the underlying structure of adversarial perturbation distributions. Unlike …

To address the issues of insufficient robustness, unstable features, and data noise
interference in existing network attack detection and identification models, this paper …