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.
翻译:进展文件中的这项工作引入了对基于自动编码器的回归神经网络(NN)模型的稳健性核查,采用最先进的方法对图像分类进行稳健性核查。尽管在开发各种深神经网络(DNNS)安全和稳健性核查方法方面不断取得进展,但尚未考虑对自动编码器模型进行稳健性检查。我们探索了这种开放的研究空间,并检查了缩小现有DNN核查方法之间差距的方法,为这些自动编码器网络扩展了现有的稳健性分析方法。虽然使用自动编码器的分类模型与图像分类 NNNP的工作或多或少相似,但回归模型的功能明显不同。我们引入了对基于自动编码器的回归模型的稳健性评价指标的两个定义,特别是百分比稳健性和不坏性等级。我们还修改了现有的图像星方法,调整了变量,以照顾回归网络的具体投入类型。该方法作为NV的扩展方法加以实施,然后应用和评价数据集,并用同样的数据集进行案例研究试验。我们引入了对自动编码模型进行首次分析,这是一份可实现性的文件。