Title: Clipping Free Attacks Against Neural Networks. Abstract: 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 fea-
ture 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 construc-
tion 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 en-
coding 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.