Deep learning (DL) has shown great success in many human-related tasks, which has led to
its adoption in many computer vision based applications, such as security surveillance …

Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Face recognition (FR) systems have demonstrated reliable verification performance,
suggesting suitability for real-world applications ranging from photo tagging in social media …

Computer vision applications like traffic monitoring, security checks, self-driving cars,
medical imaging, etc., rely heavily on machine learning models. It raises an essential …

The perception system is a safety-critical component that directly impacts the overall safety
of autonomous driving systems (ADSs). It is imperative to ensure the robustness of the deep …

Advances in speech synthesis have exposed the vulnerability of spoofing countermeasure
(CM) systems. Adversarial attacks exacerbate this problem, mainly due to the reliance of …

As the use of Deep Neural Networks (DNNs) becomes pervasive, their vulnerability to
adversarial attacks and limitations in handling unseen classes poses significant challenges …

The deep neural networks are known to be vulnerable to well-designed adversarial attacks.
The most successful defense technique based on adversarial training (AT) can achieve …

Recent studies have demonstrated that the deep neural networks (DNNs) are vulnerable to
carefully-crafted perturbations added to a legitimate input image. Such perturbed images are …