通过运行时局部鲁棒性验证进行神经网络输入验证
Input Validation for Neural Networks via Runtime Local Robustness Verification
AI总结：
本文提出一种基于运行时局部鲁棒性验证的神经网络输入验证方法，利用正确分类输入的鲁棒半径远大于错误分类输入且服从正态分布的观察，有效防御对抗样本并提升模型准确率。
摘要：
局部鲁棒性验证能够验证神经网络对特定输入在特定距离内的任何扰动是否具有鲁棒性。我们将此距离称为鲁棒半径。我们观察到，正确分类输入的鲁棒半径远大于被错误分类输入（包括对抗样本，尤其是来自强对抗攻击的样本）的鲁棒半径。另一项观察是，正确分类输入的鲁棒半径通常服从正态分布。基于这两项观察，我们提出通过运行时局部鲁棒性验证来验证神经网络的输入。实验表明，我们的方法能够保护神经网络免受对抗样本的影响，并提高其准确率。
英文摘要：
Local robustness verification can verify that a neural network is robust wrt. any perturbation to a specific input within a certain distance. We call this distance Robustness Radius. We observe that the robustness radii of correctly classified inputs are much larger than that of misclassified inputs which include adversarial examples, especially those from strong adversarial attacks. Another observation is that the robustness radii of correctly classified inputs often follow a normal distribution. Based on these two observations, we propose to validate inputs for neural networks via runtime local robustness verification. Experiments show that our approach can protect neural networks from adversarial examples and improve their accuracies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
