越野自动驾驶分割模型对抗攻击鲁棒性评估：基于数据集的视角分析
Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysis
AI总结：
本研究聚焦越野自动驾驶分割模型对抗攻击的鲁棒性，通过构建鲁棒数据集并比较不同架构，揭示数据集非鲁棒特征的影响，为提升模型安全性和机器人导航提供依据。
摘要：
本研究探讨了语义分割模型在越野自动驾驶领域中对输入扰动（对抗性攻击）的脆弱性。尽管在通用条件下表现良好，最先进的分类器往往容易受到（即使是微小的）扰动的影响，最终导致高置信度的错误预测。以往研究侧重于通过修改网络架构和使用噪声图像进行训练来增强模型鲁棒性，但未深入探讨数据集在对抗攻击中的作用。本研究旨在弥补这一空白，通过分析越野数据集中非鲁棒特征的影响，并比较不同分割网络架构下对抗攻击的效果。为此，我们构建了一个仅包含鲁棒特征的鲁棒数据集，并在此数据集上训练网络。我们展示了定性和定量分析结果，这些结果对提升越野自动驾驶应用中机器学习模型的鲁棒性具有重要意义。此外，本研究通过评估分割输出的鲁棒性，为自主机器人Unimog U5023在崎岖非结构化越野环境中的安全导航做出贡献。代码已公开于https://github.com/rohtkumar/adversarial_attacks_on_segmentation。
英文摘要：
This study investigates the vulnerability of semantic segmentation models to adversarial input perturbations, in the domain of off-road autonomous driving. Despite good performance in generic conditions, the state-of-the-art classifiers are often susceptible to (even) small perturbations, ultimately resulting in inaccurate predictions with high confidence. Prior research has directed their focus on making models more robust by modifying the architecture and training with noisy input images, but has not explored the influence of datasets in adversarial attacks. Our study aims to address this gap by examining the impact of non-robust features in off-road datasets and comparing the effects of adversarial attacks on different segmentation network architectures. To enable this, a robust dataset is created consisting of only robust features and training the networks on this robustified dataset. We present both qualitative and quantitative analysis of our findings, which have important implications on improving the robustness of machine learning models in off-road autonomous driving applications. Additionally, this work contributes to the safe navigation of autonomous robot Unimog U5023 in rough off-road unstructured environments by evaluating the robustness of segmentation outputs. The code is publicly available at https://github.com/rohtkumar/adversarial_attacks_ on_segmentation
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。
