Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks

arXiv:2609.37972 · cs.CR, cs.AI, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CR, cs.AI, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• GNNs — 25× in this paper · explained in 2 episodes
• Fidelity — 18× in this paper · explained in 2 episodes
• Decoupling — 13× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Ablation Studies — 5× in this paper · explained in 1 episode
• Embeddings — 5× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Sparsity — 4× in this paper · explained in 4 episodes
• Adversarial Examples — 3× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Over-smoothing — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Active Learning — 2× in this paper · explained in 6 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Machine Learning-as-a-Service — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Active Learning Strategy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Model — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• Adversarial Model Extraction on Graph Neural Networks
• Recent Developments in GNNs for Drug Discovery
• Graph Neural Networks Including Sparse Interpretability
• Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
• Pitfalls of Graph Neural Network Evaluation
• GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?

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