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 …

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

As Large Language Models quickly become ubiquitous, it becomes critical to understand
their security vulnerabilities. Recent work shows that text optimizers can produce …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

Nowadays, cameras equipped with AI systems can capture and analyze images to detect
people automatically. However, the AI system can make mistakes when receiving …

Most prior works on physical adversarial attacks mainly focus on the attack performance but
seldom enforce any restrictions over the appearance of the generated adversarial patches …

In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR)
plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep …

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 …

Today's proliferation of powerful facial recognition systems poses a real threat to personal
privacy. As Clearview. ai demonstrated, anyone can canvas the Internet for data and train …

Compared to query-based black-box attacks, transfer-based black-box attacks do not
require any information of the attacked models, which ensures their secrecy. However, most …