  Adversarial patches pose a realistic threat model for physical world attacks
on autonomous systems via their perception component. Autonomous systems in
safety-critical domains such as automated driving should thus contain a
fail-safe fallback component that combines certifiable robustness against
patches with efficient inference while maintaining high performance on clean
inputs. We propose BagCert, a novel combination of model architecture and
certification procedure that allows efficient certification. We derive a loss
that enables end-to-end optimization of certified robustness against patches of
different sizes and locations. On CIFAR10, BagCert certifies 10.000 examples in
43 seconds on a single GPU and obtains 86% clean and 60% certified accuracy
against 5x5 patches.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning