DNN Verification

DNN verification focuses on mathematically proving the correctness and robustness of deep neural networks (DNNs), aiming to ensure reliable performance, especially in safety-critical applications. Current research emphasizes developing efficient verification algorithms, including those based on abstract interpretation, SAT/SMT solving, and under-approximation techniques, to handle increasingly complex DNN architectures and relational properties. This field is crucial for building trust in DNNs and mitigating risks associated with their deployment in real-world systems, such as autonomous vehicles and medical diagnosis, by providing formal guarantees about their behavior.