这篇论文提出了一种能保证模型对运动模糊等卷积扰动鲁棒的训练方法，在CIFAR10上鲁棒准确率超80%，比对抗训练更强。
论文提出一种新的 Certified Training 方法，通过高效编码卷积扰动来训练可证明鲁棒的视觉模型。在 CIFAR10 上，该方法对抗合理强度的运动模糊能达到超过 80% 的鲁棒准确率，同时保持与标准训练相当的标准准确率。相比对抗训练（Adversarial Training），该方法提供了形式化的安全保证，能更有效地发现和缓解隐藏漏洞。实验表明，该方法在多种卷积扰动下显著优于对抗训练。
Certified Training for Convolutional Perturbations
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 since phenomena such as slightly blurred vision might lead to failures, for example an object detector missing objects. While methods such as data augmentation or Adversarial Training can improve empirical robustness, they lack formal safety guarantees, making it difficult to identify and mitigate hidden vulnerabilities. We introduce a novel Certified Training approach that leverages an efficient encoding of convolutional perturbations to train provably robust models. Our method significantly outperforms Adversarial Training, achieving, for example, over 80% robust accuracy against motion blur of reasonable intensity on CIFAR10 while maintaining comparable standard accuracy.