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We propose a robust variant of boosting forest to the various adversarialdefense methods, and apply it to enhance the robustness of the deep neuralnetwork. We retain the deep network architecture, weights, and middle layerfeatures, then install gradient boosting forest to select the features fromeach layer of the deep network, and predict the target. For training eachdecision tree, we propose a novel conservative and greedy trade-off, withconsideration for less misprediction instead of pure gain functions, thereforebeing suboptimal and conservative. We actively increase tree depth to remedythe accuracy with splits in more features, being more greedy in growing treedepth. We propose a new task on 3D face model, whose robustness has not beencarefully studied, despite the great security and privacy concerns related toface analytics. We tried a simple attack method on a pure convolutional neuralnetwork (CNN) face shape estimator, making it degenerate to only output averageface shape with invisible perturbation. Our conservative-greedy boosting forest(CGBF) on face landmark datasets showed a great improvement over original puredeep learning methods under the adversarial attacks.

Robust Boosting Forests with Richer Deep Feature Hierarchy