Efficient Certified Defenses Against Patch Attacks on Image ClassifiersDownload PDF

Published: 12 Jan 2021, Last Modified: 05 May 2023ICLR 2021 PosterReaders: Everyone
Keywords: robustness, certified defense, adversarial patch, aversarial examples
Abstract: 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.
One-sentence Summary: We propose a method for certifying robustness against adversarial patches that combines high certified accuracy with efficient inference while maintaining strong performance on clean data.
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Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10)
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