Using Quantifier Elimination to Enhance the Safety Assurance of Deep Neural Networks
This work addresses safety assurance for DNNs in critical applications like aerospace, but it is incremental as it builds on existing formal methods.
The paper tackled the challenge of ensuring safe operation of deep neural networks in safety-critical domains by proposing quantifier elimination as a complementary technique to existing verification methods, using an airborne collision avoidance DNN as a case example to illustrate its application for range forward propagation and robustness analysis.
Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has enabled rapid development of sophisticated and autonomous systems. However, the inherent complexity to rigorously assure the safe operation of such systems hinders their real-world adoption in safety-critical domains such as aerospace and medical devices. Hence, there is a surge in interest to explore the use of advanced mathematical techniques such as formal methods to address this challenge. In fact, the initial results of such efforts are promising. Along these lines, we propose the use of quantifier elimination (QE) - a formal method technique, as a complimentary technique to the state-of-the-art static analysis and verification procedures. Using an airborne collision avoidance DNN as a case example, we illustrate the use of QE to formulate the precise range forward propagation through a network as well as analyze its robustness. We discuss the initial results of this ongoing work and explore the future possibilities of extending this approach and/or integrating it with other approaches to perform advanced safety assurance of DNNs.