Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
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Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Despite the great successes achieved by deep neural networks (DNNs), recent studies show that they are vulnerable against adversarial examples, which aim to mislead DNNs by adding small adversarial perturbations. Several defenses have been proposed against such attacks, while many of them have been adaptively attacked. In this work, we aim to enhance the ML robustness from a different perspective by leveraging domain knowledge: We propose a Knowledge Enhanced Machine Learning Pipeline (KEMLP) to integrate domain knowledge (i.e., logic relationships among different predictions) into a probabilistic graphical model via first-order logic rules. In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task. Theoretically, we provide convergence results and prove that, under mild conditions, the prediction of KEMLP is more robust than that of the main DNN model. Empirically, we take road sign recognition as an example and leverage the relationships between road signs and their shapes and contents as domain knowledge. We show that compared with adversarial training and other baselines, KEMLP achieves higher robustness against physical attacks, L-p bounded attacks, unforeseen attacks, and natural corruptions under both whitebox and blackbox settings, while still maintaining high clean accuracy. Show more
Publication status
publishedExternal links
Book title
Proceedings of the 38th International Conference on Machine LearningJournal / series
Proceedings of Machine Learning ResearchVolume
Pages / Article No.
Publisher
PMLREvent
Organisational unit
09588 - Zhang, Ce (ehemalig) / Zhang, Ce (former)
Funding
184628 - EASEML: Toward a More Accessible and Usable Machine Learning Platform for Non-expert Users (SNF)
957407 - Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning (EC)
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ETH Bibliography
yes
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