Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure
  Detection
Jonathan J. Y. Kim Martin Urschler Patricia J. Riddle Jorg S. Wicker
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.