Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection

TL;DR

This paper introduces BOCLOAK, a novel optimal transport-based framework for generating realistic adversarial attacks on GNN-based social bot detectors, revealing their vulnerabilities under real-world constraints.

Contribution

BOCLOAK is the first method to leverage optimal transport for constraint-aware adversarial attacks on GNN-based bot detection, improving attack success and efficiency.

Findings

BOCLOAK achieves up to 80.13% higher attack success rates.

Uses 99.80% less GPU memory compared to baselines.

Reveals vulnerabilities of GNN-based detectors under realistic constraints.

Abstract

The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingly rely on Graph Neural Networks (GNNs). However, the effectiveness of these GNN-based detectors in real-world settings remains poorly understood. In practice, attackers continuously adapt their strategies as well as must operate under domain-specific and temporal constraints, which can fundamentally limit the applicability of existing attack methods. As a result, there is a critical need for robust GNN-based bot detection methods under realistic, constraint-aware attack scenarios. To address this gap, we introduce BOCLOAK to systematically evaluate the robustness of GNN-based social bot detection via both edge editing and node injection adversarial attacks under realistic constraints. BOCLOAK constructs a probability measure over spatio-temporal neighbor features and learns an optimal transport geometry that separates human and bot behaviors. It then decodes transport plans into sparse, plausible edge edits that evade detection while obeying real-world constraints. We evaluate BOCLOAK across three social bot datasets, five state-of-the-art bot detectors, three adversarial defenses, and compare it against four leading graph adversarial attack baselines. BOCLOAK achieves up to 80.13% higher attack success rates while using 99.80% less GPU memory under realistic real-world constraints. Most importantly, BOCLOAK shows that optimal transport provides a lightweight, principled framework for bridging the gap between adversarial attacks and real-world bot detection.