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CARD: Certifiably Robust Machine Learning Pipeline via Domain Knowledge Integration

The advent of ubiquitous machine learning (ML) has led to exciting revolution in computing today. However, recent studies have shown that ML, especially deep neural networks (DNNs), are vulnerable to adversarial examples, which are able to mislead DNNs with carefully crafted stealthy perturbations. So far, many defense approaches have been proposed against such adversarial attacks, both empirically and theoretically. Though effective under certain conditions, existing empirical defenses are usually found vulnerable against new attacks; existing certified defenses are only able to certify robustness against limited perturbation radius. As current pure data-driven defenses have reached a bottleneck towards certifiably robust ML, in this paper we propose a certifiably robust ML pipeline CARD, aiming to integrate exogenous information, such as domain knowledge, as logical rules with ML models to improve the certified robustness. Intuitively, domain knowledge (e.g., cat belongs to the animal category) will prevent attacks that violate these knowledge rules, and it is also challenging to construct adaptive attacks satisfying such pre-defined logical relationships. In particular, we express the domain knowledge as first-order logic rules and embed these logic rules in a probabilistic graphical model. We then prove that such a probabilistic graphical model can be mapped to a 1-layer NN for efficient training. We conduct extensive experiments on several high-dimensional datasets and show that our proposed CARD achieves the state-of-the-art certified robustness.

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