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.

本文使用一种名为对抗性样本的设计，产生一个可以同时攻击深度神经网络的通用攻击，包括目标攻击，通用攻击以及可转移攻击。

通用、可转移和有针对性的对抗攻击

Universal, transferable and targeted adversarial attacks

Neural networks are prone to adversarial attacks. In general, such attacks
deteriorate the quality of the input by either slightly modifying most of its
pixels, or by occluding it with a patch. In this paper, we propose a method
that keeps the image unchanged and only adds an adversarial framing on the
border of the image. We show empirically that our method is able to
successfully attack state-of-the-art methods on both image and video
classification problems. Notably, the proposed method results in a universal
attack which is very fast at test time. Source code can be found at
this https URL .

本文提出一种使用边框添加对抗性干扰的图像覆盖方法， 能够成功攻击最先进的图像和视频分类方法，从而对神经网络的安全性提出挑战，并提供了源代码。