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Average Certified Radius is a Poor Metric for Randomized Smoothing
Chenhao Sun, Yuhao Mao, Mark Niklas Müller +1
Randomized smoothing (RS) is popular for providing certified robustness guarantees against adversarial attacks. The average certified radius (ACR) has emerged as a widely used metr…
Expressiveness of Multi-Neuron Convex Relaxations in Neural Network Certification
Yuhao Mao, Yani Zhang, Martin Vechev
Neural network certification methods heavily rely on convex relaxations to provide robustness guarantees. However, these relaxations are often imprecise: even the most accurate sin…
CTBENCH: A Library and Benchmark for Certified Training
Yuhao Mao, Stefan Balauca, Martin Vechev
Training certifiably robust neural networks is an important but challenging task. While many algorithms for (deterministic) certified training have been proposed, they are often ev…
Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations
Stefan Balauca, Mark Niklas Müller, Yuhao Mao +3
Training neural networks with high certified accuracy against adversarial examples remains an open challenge despite significant efforts. While certification methods can effectivel…