Abstract:
Formal robustness verification of neural networks is essential for deploying machine learning (ML) systems in safety-critical applications. However, existing verification frameworks are predominantly software-based, incurring prohibitive computational costs and long runtimes, severely limiting scalability and practical deployment. Although symbolic interval analysis enables scalable over-approximate verification, its effectiveness is limited by …