安全なロボットナビゲーションのための視覚的位置認識における敵対的攻撃と検出
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation
視覚的位置認識(VPR)に対する4つの一般的な敵対的攻撃と4つのVPR特有の新規攻撃の影響を分析し、敵対的攻撃検出器(AAD)をVPRとナビゲーション判断のループに組み込む枠組みを提案した。検出精度75%でも平均位置誤差を約50%削減できることを示し、FGSM攻撃のVPRへの有効性も初めて調査した。
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著者: Connor Malone, Owen Claxton, Iman Shames, Michael Milford
分類: cs.CV, cs.RO
原文アブストラクト
Stand-alone Visual Place Recognition (VPR) systems have little defence against a well-designed adversarial attack, which can lead to disastrous consequences when deployed for robot navigation. This paper extensively analyzes the effect of four adversarial attacks common in other perception tasks and four novel VPR-specific attacks on VPR localization performance. We then propose how to close the loop between VPR, an Adversarial Attack Detector (AAD), and active navigation decisions by demonstrating the performance benefit of simulated AADs in a novel experiment paradigm -- which we detail for the robotics community to use as a system framework. In the proposed experiment paradigm, we see the addition of AADs across a range of detection accuracies can improve performance over baseline; demonstrating a significant improvement -- such as a ~50% reduction in the mean along-track localization error -- can be achieved with True Positive and False Positive detection rates of only 75% and up to 25% respectively. We examine a variety of metrics including: Along-Track Error, Percentage of Time Attacked, Percentage of Time in an `Unsafe' State, and Longest Continuous Time Under Attack. Expanding further on these results, we provide the first investigation into the efficacy of the Fast Gradient Sign Method (FGSM) adversarial attack for VPR. The analysis in this work highlights the need for AADs in real-world systems for trustworthy navigation, and informs quantitative requirements for system design.
