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 success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

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 …

Deep learning methods for graphs achieve remarkable performance on many tasks.
However, despite the proliferation of such methods and their success, recent findings …

We study the robustness of reinforcement learning (RL) with adversarially perturbed state
observations, which aligns with the setting of many adversarial attacks to deep …

Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be
particularly vulnerable to adversarial perturbations on input observations. Therefore, it is …

The rapid growth in the number of devices and their connectivity has enlarged the attack
surface and made cyber systems more vulnerable. As attackers become increasingly …

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual …

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the
strongest/optimal adversarial perturbations on state observations (within some constraints) …

Machine learning (ML) has been universally adopted for automated decisions in a variety of
fields, including recognition and classification applications, recommendation systems …