The main challenges are discussed together with the lessons learned from past and
ongoing research along the development cycle of machine learning systems. This will be …

The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that
need to be addressed to ensure secure and trustworthy socio-technical infrastructures …

Background and objective: Training a deep convolutional neural network (CNN) for
automatic image classification requires a large database with images of labeled samples …

We propose a security methodology for Machine Learning (ML) pipelines, supporting the
definition of key security properties of ML assets, the identification of threats to them as well …

Training images with data transformations, eg, crops, shifts, rotations and color distortions,
have been suggested as contrastive examples to evaluate the robustness of deep neural …

With the increasing deployment of deep neural networks in safety-critical applications such
as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has …

Training images with data transformations have been suggested as contrastive examples to
complement the testing set for generalization performance evaluation of deep neural …

The recent years have been marked by extended research on adversarial attacks, especially
on deep neural networks. With this work we intend on posing and investigating the question …

Data Augmentation (DA) methods are widely-used in various areas of machine learning,
and have been associated with the generalization capabilities of deep neural networks …

Robustness is an important, and yet, under-explored aspect of unsupervised representation
learning, which has seen a lot of recent developments. In this work, we address this gap by …