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The first thing to do is to develop and test the event recovery process to ensure that appropriate responses are made when caught off guard. This includes, but is not limited to, having the necessary controls to delay or stop processing and knowing which to call when debugging the classifier.项羽看着天空，呆呆出神，许久问道：亚父，那两颗星辰是何人？范增高深莫测一笑，说道：你知道的，必须来问老夫。劇中以派出所為舞台，描述基層警察中發生的故事……Bulgaria: 275,000板栗还在书房看书，张槐吩咐他立即去叫郑氏过来。　　改编自朱东根的人气网络漫画《极度恐慌》。From the defender's point of view, this type of attack has proved (so far) to be very problematic, because we do not have effective methods to defend against this type of attack. Fundamentally speaking, we do not have an effective way for DNN to produce good output for all inputs. It is very difficult for them to do so, because DNN performs nonlinear/nonconvex optimization in a very large space, and we have not taught them to learn generalized high-level representations. You can read Ian and Nicolas's in-depth articles (http://www.cleverhans.io/security/privacy/ml/2017/02/15/why-attaching-machine-learning-is-easier-than-defending-it.html) to learn more about this.