  Neural network ensembles have been studied extensively in the context of
adversarial robustness and most ensemble-based approaches remain vulnerable to
adaptive attacks. In this paper, we investigate the robustness of
Error-Correcting Output Codes (ECOC) ensembles through architectural
improvements and ensemble diversity promotion. We perform a comprehensive
robustness assessment against adaptive attacks and investigate the relationship
between ensemble diversity and robustness. Our results demonstrate the benefits
of ECOC ensembles for adversarial robustness compared to regular ensembles of
convolutional neural networks (CNNs) and show why the robustness of previous
implementations is limited. We also propose an adversarial training method
specific to ECOC ensembles that allows to further improve robustness to
adaptive attacks.
