Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Social media are nowadays one of the main news sources for millions of people around the
globe due to their low cost, easy access and rapid dissemination. This however comes at the …

Deep learning achieves state-of-the-art results in many tasks in computer vision and natural
language processing. However, recent works have shown that deep networks can be …

Event cameras encode visual information with high temporal precision, low data-rate, and
high-dynamic range. Thanks to these characteristics, event cameras are particularly suited …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Detection of adversarial examples with high accuracy is critical for the security of deployed
deep neural network-based models. We present the first graph-based adversarial detection …

It is well-known that classifiers are vulnerable to adversarial perturbations. To defend
against adversarial perturbations, various certified robustness results have been derived …

This paper introduces a neural style transfer model to generate a stylized image
conditioning on a set of examples describing the desired style. The proposed solution …

Although deep neural networks (DNN) have achieved great success, their applications in
safety-critical areas are hindered due to their vulnerability to adversarial attacks. Some …