Deep Neural networks (DNNs) are challenged by their vulnerability to adversarial examples, which are crafted by adding human-imperceptible noises to real examples, but make a model output inaccurate predictions.

Researches on adversarial attacks and defenses are the foundations of building robust artificial intelligence systems.