  Many works have investigated the adversarial attacks or defenses under the
settings where a bounded and imperceptible perturbation can be added to the
input. However in the real-world, the attacker does not need to comply with
this restriction. In fact, more threats to the deep model come from
unrestricted adversarial examples, that is, the attacker makes large and
visible modifications on the image, which causes the model classifying
mistakenly, but does not affect the normal observation in human perspective.
Unrestricted adversarial attack is a popular and practical direction but has
not been studied thoroughly. We organize this competition with the purpose of
exploring more effective unrestricted adversarial attack algorithm, so as to
accelerate the academical research on the model robustness under stronger
unbounded attacks. The competition is held on the TianChi platform
(\url{https://tianchi.aliyun.com/competition/entrance/531853/introduction}) as
one of the series of AI Security Challengers Program.
