  Deep Neural Networks have been found vulnerable re-cently. A kind of
well-designed inputs, which called adver-sarial examples, can lead the networks
to make incorrectpredictions. Depending on the different scenarios, goalsand
capabilities, the difficulties of the attacks are different.For example, a
targeted attack is more difficult than a non-targeted attack, a universal
attack is more difficult than anon-universal attack, a transferable attack is
more difficultthan a nontransferable one. The question is: Is there existan
attack that can meet all these requirements? In this pa-per, we answer this
question by producing a kind of attacksunder these conditions. We learn a
universal mapping tomap the sources to the adversarial examples. These
exam-ples can fool classification networks to classify all of theminto one
targeted class, and also have strong transferability.Our code is released at:
xxxxx.
