Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool

Abstract

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.

In Proceedings SNR 2021, arXiv:2207.04391

References in corpus (4)

• An approach to reachability analysis for feed-forward ReLU neural networks
• Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
• Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
• Property-Directed Verification of Recurrent Neural Networks