  In this paper, we address a problem of machine learning system vulnerability
to adversarial attacks. We propose and investigate a Key based Diversified
Aggregation (KDA) mechanism as a defense strategy. The KDA assumes that the
attacker (i) knows the architecture of classifier and the used defense
strategy, (ii) has an access to the training data set but (iii) does not know
the secret key. The robustness of the system is achieved by a specially
designed key based randomization. The proposed randomization prevents the
gradients' back propagation or the creating of a "bypass" system. The
randomization is performed simultaneously in several channels and a
multi-channel aggregation stabilizes the results of randomization by
aggregating soft outputs from each classifier in multi-channel system. The
performed experimental evaluation demonstrates a high robustness and
universality of the KDA against the most efficient gradient based attacks like
those proposed by N. Carlini and D. Wagner and the non-gradient based sparse
adversarial perturbations like OnePixel attacks.
