6 papers
Certified Training for Convolutional Perturbations
Benedikt Brückner, Alessio Lomuscio
Vision models have been found to be susceptible to perturbations such as motion blur induced at runtime by a shaking camera. This impedes their deployment in critical applications…
Hybrid Robustness Verification for Spatio-Temporal Neural Networks
Sherwin Varghese, Matthew Wicker, Alessio Lomuscio
With AI increasingly deployed in safety-critical systems, providing formal robustness guarantees for the underlying models is essential. Existing verification methods either rely o…
Lipschitz Optimization for Formal Verification of Homographies
Jean-Guillaume Durand, Panagiotis Kouvaros, Maxime Gariel +1
The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety-critical domains such as healthcare, autonomous vehicles,…
A Robust Out-of-Distribution Detection Framework via Synergistic Smoothing
Maria Stoica, Abdelrahman Hekal, Alessio Lomuscio
Reliable out-of-distribution (OOD) detection is a critical requirement for the safe deployment of machine learning systems. Despite recent progress, state-of-the-art OOD detectors…
IoUCert: Robustness Verification for Anchor-based Object Detectors
Benedikt Brückner, Alejandro J. Mercado, Yanghao Zhang +2
While formal robustness verification has seen significant success in image classification, scaling these guarantees to object detection remains notoriously difficult due to complex…
Out-of-Distribution Detection using Counterfactual Distance
Maria Stoica, Francesco Leofante, Alessio Lomuscio
Accurate and explainable out-of-distribution (OOD) detection is required to use machine learning systems safely. Previous work has shown that feature distance to decision boundarie…