This paper presents the RADNN algorithm. The RADNN is a robust to
imperceptible adversarial attack algorithm that uses the concept of data
density and similarities to detect attacks on real-time. Differently
from traditional deep learnings that need be trained on the attacks to
be able to detect, RADNN has a mechanism that detects data patterns
changes. In order to evaluate the proposed method, we considered the
PerC attacks and a 1000 images from the Imagenet dataset. The RADNN
could correctly identify 97.2% of the attacks.