Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness

We develop a three - step diversity ensemble creation algorithm: (1) Creating a pool of candidate ensemble member models, or so called base models; (2) Creating a pool of candidate ensemble teams with their diversity scores higher than the pre - defined minimum diversity threshold; and (3) Developing robust ensemble consensus methods, which can effectively combine, rank and integrate predictions from members of an ensemble committee to produce high accuracy ensemble prediction output again st adversarial examples. D ifferent ensemble creation methods tend to have varying level of diversity. A. Creating Ensemble s of Type 1 diversity We want to construct a pool of N redundant DNN models trained on the same learning task as the base classifiers. Preferably, the best ensemble committee members are those base classifiers that are relatively diverse and have high individual test accuracy. T he type 1 diversity ensemble creation algorithm requires that every base model in the pool meet s the type 1 dive rsity and ha s high benign test accuracy comparable to that of the target model under protection. One approach is to add one member model to the pool at a time. Assume that we initialize the pool with a privately trained DNN model. We only allow the next mo del to be added to the pool if it is trained independently using different hyper - parameters or different neural network structures or algorithms and it meet s the high benign test accuracy requirement.