  The vulnerability of machine learning systems to adversarial attacks
questions their usage in many applications. In this paper, we propose a
randomized diversification as a defense strategy. We introduce a multi-channel
architecture in a gray-box scenario, which assumes that the architecture of the
classifier and the training data set are known to the attacker. The attacker
does not only have access to a secret key and to the internal states of the
system at the test time. The defender processes an input in multiple channels.
Each channel introduces its own randomization in a special transform domain
based on a secret key shared between the training and testing stages. Such a
transform based randomization with a shared key preserves the gradients in
key-defined sub-spaces for the defender but it prevents gradient back
propagation and the creation of various bypass systems for the attacker. An
additional benefit of multi-channel randomization is the aggregation that fuses
soft-outputs from all channels, thus increasing the reliability of the final
score. The sharing of a secret key creates an information advantage to the
defender. Experimental evaluation demonstrates an increased robustness of the
proposed method to a number of known state-of-the-art attacks.
