  During the last years, a remarkable breakthrough has been made in AI domain
thanks to artificial deep neural networks that achieved a great success in many
machine learning tasks in computer vision, natural language processing, speech
recognition, malware detection and so on. However, they are highly vulnerable
to easily crafted adversarial examples. Many investigations have pointed out
this fact and different approaches have been proposed to generate attacks while
adding a limited perturbation to the original data. The most robust known
method so far is the so called C&W attack [1]. Nonetheless, a countermeasure
known as feature squeezing coupled with ensemble defense showed that most of
these attacks can be destroyed [6]. In this paper, we present a new method we
call Centered Initial Attack (CIA) whose advantage is twofold : first, it
insures by construction the maximum perturbation to be smaller than a threshold
fixed beforehand, without the clipping process that degrades the quality of
attacks. Second, it is robust against recently introduced defenses such as
feature squeezing, JPEG encoding and even against a voting ensemble of
defenses. While its application is not limited to images, we illustrate this
using five of the current best classifiers on ImageNet dataset among which two
are adversarialy retrained on purpose to be robust against attacks. With a
fixed maximum perturbation of only 1.5% on any pixel, around 80% of attacks
(targeted) fool the voting ensemble defense and nearly 100% when the
perturbation is only 6%. While this shows how it is difficult to defend against
CIA attacks, the last section of the paper gives some guidelines to limit their
impact.
