Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
This addresses security vulnerabilities in KGE models, which are widely used, but the approach is incremental as it builds on existing attack methods.
The paper tackles the problem of data poisoning attacks on Knowledge Graph Embeddings (KGE) for link prediction by proposing adversarial deletions using instance attribution methods and heuristic additions, resulting in up to 62% improvement in MRR degradation over baselines.
Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poisoning attacks against KGE models for link prediction. These attacks craft adversarial additions or deletions at training time to cause model failure at test time. To select adversarial deletions, we propose to use the model-agnostic instance attribution methods from Interpretable Machine Learning, which identify the training instances that are most influential to a neural model's predictions on test instances. We use these influential triples as adversarial deletions. We further propose a heuristic method to replace one of the two entities in each influential triple to generate adversarial additions. Our experiments show that the proposed strategies outperform the state-of-art data poisoning attacks on KGE models and improve the MRR degradation due to the attacks by up to 62% over the baselines.