论文标题
正在进行的工作：使用NNV工具对基于自动编码器的回归模型的安全性和稳健性验证
Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool
论文作者
论文摘要
这项工作在进度论文中介绍了基于自动编码器的回归神经网络（NN）模型的鲁棒性验证，遵循最新方法的稳健性验证图像分类NNS的方法。尽管在各种深层神经网络（DNN）中开发验证方法的验证方法方面取得了持续的进展，但尚未考虑对自动编码器模型的稳健性检查。我们通过扩展此类自动编码器网络的现有鲁棒性分析方法来探索研究的开放空间，并检查如何弥合现有DNN验证方法之间的差距。尽管使用自动编码器的分类模型或多或少地与图像分类NN相似，但回归模型的功能却明显不同。我们介绍了基于自动编码器的回归模型的鲁棒性评估指标的两个定义，特别是鲁棒性和非舒适性等级。我们还修改了现有的Imagestar方法，调整了变量以照顾回归网络的特定输入类型。该方法被作为NNV的扩展实现，然后在数据集上应用和评估，并在使用相同数据集的案例研究实验上实现了该方法。根据作者的理解，这项在进度论文中是第一个显示基于自动编码器NNS的可及性分析的方法。
This work in progress paper introduces robustness verification for autoencoder-based regression neural network (NN) models, following state-of-the-art approaches for robustness verification of image classification NNs. Despite the ongoing progress in developing verification methods for safety and robustness in various deep neural networks (DNNs), robustness checking of autoencoder models has not yet been considered. We explore this open space of research and check ways to bridge the gap between existing DNN verification methods by extending existing robustness analysis methods for such autoencoder networks. While classification models using autoencoders work more or less similar to image classification NNs, the functionality of regression models is distinctly different. We introduce two definitions of robustness evaluation metrics for autoencoder-based regression models, specifically the percentage robustness and un-robustness grade. We also modified the existing Imagestar approach, adjusting the variables to take care of the specific input types for regression networks. The approach is implemented as an extension of NNV, then applied and evaluated on a dataset, with a case study experiment shown using the same dataset. As per the authors' understanding, this work in progress paper is the first to show possible reachability analysis of autoencoder-based NNs.
