Deep neural networks have recently been found to be vulnerable to adversarial examples,
which can deceive attacked models with high confidence. This has given rise to significant …

Adversarial examples demonstrate the vulnerability of white-box models but exhibit weak
transferability to black-box models. In image processing, each adversarial example usually …

Deep learning models are highly vulnerable to adversarial examples, leading to significant
attention on techniques for detecting them. However, current methods primarily rely on …

Abstract Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples,
the detection of which is crucial for the wide application of these DNN models. While existing …

Artificial neural networks are prone to being fooled by carefully perturbed inputs which
cause an egregious misclassification. These adversarial attacks have been the focus of …

This thesis explores the application of Federated Learning (FL) in healthcare and medical
imaging, addressing the key challenge of utilizing large, dispersed medical datasets while …

In November of 2022, the European Union Agency for Cybersecurity (ENISA) released its
2022 ENISA Threat Landscape Report (ETL), which describes the observed threats in the …