用条件随机场提升GNN推理阶段的抗攻击能力
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
- 基于条件随机场构建模型无关的后处理防御机制
- 在多个基准数据集上显著提升GNN的对抗鲁棒性
- 适合部署时增强现有GNN模型的安全性
图神经网络（GNN）作为图表示学习的当前主流方法，已被证明对对抗攻击敏感，影响其实际应用。现有防御方法多集中于训练阶段，如调整消息传递结构或预处理，但缺乏针对推理阶段的增强策略。本文提出RobustCRF，一种基于条件随机场的后处理方法，可在不依赖模型结构信息的前提下，在推理阶段提升GNN的鲁棒性。该方法具有模型无关性，适用于多种现有GNN模型。我们在多个基准节点分类数据集上验证了其有效性，结果表明该方法能显著增强模型在对抗攻击下的稳定性。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns about their real-world applicability. While existing defense techniques primarily concentrate on the training phase of GNNs, involving adjustments to message passing architectures or pre-processing methods, there is a noticeable gap in methods focusing on increasing robustness during inference. In this context, this study introduces RobustCRF, a post-hoc approach aiming to enhance the robustness of GNNs at the inference stage. Our proposed method, founded on statistical relational learning using a Conditional Random Field, is model-agnostic and does not require prior knowledge about the underlying model architecture. We validate the efficacy of this approach across various models, leveraging benchmark node classification datasets.
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