Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have
been widely studied, and various defenses were proposed. However, the possibility and …

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 …

Adversarial examples have been shown to exist for a variety of deep learning architectures.
Deep reinforcement learning has shown promising results on training agent policies directly …

Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to
adversarial attacks, which attack DRL models by adding small perturbations to the …

We introduce two tactics to attack agents trained by deep reinforcement learning algorithms
using adversarial examples, namely the strategically-timed attack and the enchanting attack …

Deep learning has evolved as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Reinforcement learning is a set of goal-oriented learning algorithms, through which an agent
could learn to behave in an environment, by performing certain actions and observing the …

Deep learning has emerged as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Recent studies have shown that deep reinforcement learning agents are vulnerable to small
adversarial perturbations on the agent's inputs, which raises concerns about deploying such …

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques,
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …