  The intriguing phenomenon of adversarial examples has attracted significant
attention in machine learning and what might be more surprising to the
community is the existence of universal adversarial perturbations (UAPs), i.e.
a single perturbation to fool the target DNN for most images. With the focus on
UAP against deep classifiers, this survey summarizes the recent progress on
universal adversarial attacks, discussing the challenges from both the attack
and defense sides, as well as the reason for the existence of UAP. We aim to
extend this work as a dynamic survey that will regularly update its content to
follow new works regarding UAP or universal attack in a wide range of domains,
such as image, audio, video, text, etc. Relevant updates will be discussed at:
https://bit.ly/2SbQlLG. We welcome authors of future works in this field to
contact us for including your new finding.
