New Publication: Towards Formal Security Analysis of Deep Neural Network Architectures
Our paper,”Towards Formal Security Analysis of Deep Neural Network Architectures,” has been published in .
Neural networks are increasingly used in security-critical domains, yet their susceptibility to attacks and manipulation remains a key challenge. In this work, we investigate the use of formal techniques to verify security-related properties of deep neural network architectures, contributing toward more rigorous assurance frameworks for AI-enabled systems.
This is joint work with researchers from and .
See Publications for more details!