CAREER: DeepTrust: Enabling Robust Machine Learning with Exogenous Information

Great advances in machine learning have led to state-of-the-art performance on a wide range of tasks, such as image classification, machine translation, and robotics. However, recent studies have shown that when machine learning models are exposed to adversarial attacks, they can be fooled, evaded, and misled in ways that would have profound security implications: image recognition, natural language processing, and audio recognition systems have all been attacked recently.

As machine learning techniques are incorporated into safety-critical systems, from financial systems to self-driving cars to medical diagnosis, it is vitally important to develop trustworthy and robust learning approaches for massive production and deployment of safety-critical machine learning applications. Although there have been exciting progresses in the area of robust learning, there is still a long way to go considering sophisticated real-world adversaries.

Thus, in this project the investigator aims to gain fundamental understandings about adversarial properties and constraints, and develop machine learning systems with robustness guarantees for different real-world applications.

One limitation of existing learning methods is inherent in the fact that most existing methods have been treating machine learning as a “pure data-driven technique” that solely depends on a given training-set, without interacting with their rich exogenous information that is not fully modeled by the data itself. This project aims to design novel techniques to incorporate exogenous information in machine learning systems.

In particular, this project includes three aims, each of which addresses a unique challenge of understanding and integrating exogenous information to design robust machine learning systems: (1) the team of researchers will first focus on understanding of intrinsic information such as model viability and leverage it to design certifiably robust machine learning models/ensembles, (2) then the researchers will focus on the extrinsic information such as domain knowledge to design certifiably robust machine learning pipelines, (3) finally the researchers will apply the proposed techniques to two safety-critical applications, adversarial multimedia data detection and robust autonomous vehicles, to demonstrate the practicality of the proposed research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.