Attacking the Loop: Adversarial Attacks on Graph-Based Loop Closure Detection

Jonathan Kim, Jonathan Kim, Martin Urschler, Martin Urschler, Patricia Riddle, Jörg Wicker

2024

Abstract

With the advancement in robotics, it is becoming increasingly common for large factories and warehouses to incorporate visual SLAM (vSLAM) enabled automated robots that operate closely next to humans. This makes any adversarial attacks on vSLAM components potentially detrimental to humans working alongside them. Loop Closure Detection (LCD) is a crucial component in vSLAM that minimizes the accumulation of drift in mapping, since even a small drift can accumulate into a significant drift over time. A prior work by Kim et al., SymbioLCD2, unified visual features and semantic objects into a single graph structure for finding loop closure candidates. While this provided a performance improvement over visual feature-based LCD, it also created a single point of vulnerability for potential graph-based adversarial attacks. Unlike previously reported visual-patch based attacks, small graph perturbations are far more challenging to detect, making them a more significant threat. In this paper, we present Adversarial-LCD, a novel black-box evasion attack framework that employs an eigencentrality-based perturbation method and an SVM-RBF surrogate model with a Weisfeiler-Lehman feature extractor for attacking graph-based LCD. Our evaluation shows that the attack performance of Adversarial-LCD with the SVM-RBF surrogate model was superior to that of other machine learning surrogate algorithms, including SVM-linear, SVM-polynomial, and Bayesian classifier, demonstrating the effectiveness of our attack framework. Furthermore, we show that our eigencentrality-based perturbation method outperforms other algorithms, such as Random-walk and Shortest-path, highlighting the efficiency of Adversarial-LCD’s perturbation selection method.

Paper Citation

in Harvard Style

Kim J., Urschler M., Riddle P. and Wicker J. (2024). Attacking the Loop: Adversarial Attacks on Graph-Based Loop Closure Detection. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 90-97. DOI: 10.5220/0012313100003660

in Bibtex Style

@conference{visapp24,
author={Jonathan Kim and Martin Urschler and Patricia Riddle and Jörg Wicker},
title={Attacking the Loop: Adversarial Attacks on Graph-Based Loop Closure Detection},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={90-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012313100003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Attacking the Loop: Adversarial Attacks on Graph-Based Loop Closure Detection
SN - 978-989-758-679-8
AU - Kim J.
AU - Urschler M.
AU - Riddle P.
AU - Wicker J.
PY - 2024
SP - 90
EP - 97
DO - 10.5220/0012313100003660
PB - SciTePress