  Social media platforms provide an environment where people can freely engage
in discussions. Unfortunately, they also enable several problems, such as
online harassment. Recently, Google and Jigsaw started a project called
Perspective, which uses machine learning to automatically detect toxic
language. A demonstration website has been also launched, which allows anyone
to type a phrase in the interface and instantaneously see the toxicity score
[1]. In this paper, we propose an attack on the Perspective toxic detection
system based on the adversarial examples. We show that an adversary can subtly
modify a highly toxic phrase in a way that the system assigns significantly
lower toxicity score to it. We apply the attack on the sample phrases provided
in the Perspective website and show that we can consistently reduce the
toxicity scores to the level of the non-toxic phrases. The existence of such
adversarial examples is very harmful for toxic detection systems and seriously
undermines their usability.
