Anahita Baninajjar — accepted AI-conference papers

2024

VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees

Anahita Baninajjar, Ahmed Rezine, Amir Aminifar

ICML 2024poster

Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2023

SafeDeep: A Scalable Robustness Verification Framework for Deep Neural Networks

Anahita Baninajjar, Kamran Hosseini, Ahmed Rezine, Amir Aminifar

ICASSP 2023accepted

The state-of-the-art machine learning techniques come with limited, if at all any, formal correctness guarantees. This has been demonstrated by adversarial examples in the deep learning domain. To address this challenge, here, we propose a scalable robustness verification framework for Deep Neural N…

Cited by 0 Source ↗Scholar ↗