We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| seaocn/motif-backdoor | canonical | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| pass_data_iteratively | Not yet run | seaocn/motif-backdoor/attack_main.py pointer only (licence: NONE) · get_code("8e19c09bbe42a87f") |
| test | Not yet run | seaocn/motif-backdoor/attack_main.py pointer only (licence: NONE) · get_code("7071815a9ba67b6d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas, such as biological gene prediction, social recommendation, etc. Recent works have exposed that GNN is vulnerable to the backdoor attack, i.e., models trained with maliciously crafted training samples are easily fooled by patched samples. Most of the proposed studies launch the backdoor attack using a trigger that either is the randomly generated subgraph (e.g., erdős-rényi backdoor) for less computational burden, or the gradient-based generative subgraph (e.g., graph trojaning attack) to enable a more effective attack. However, the interpretation of how is the trigger structure and the effect of the backdoor attack related has been overlooked in the current literature. Motifs, recurrent and statistically significant sub-graphs in graphs, contain rich structure information. In this paper, we are rethinking the trigger from the perspective of motifs, and propose a motif-based backdoor attack, denoted as Motif-Backdoor. It contributes from three aspects. (i) Interpretation: it provides an in-depth explanation for backdoor effectiveness by the validity of the trigger structure from motifs, leading to some novel insights, e.g., using subgraphs that appear less frequently in the graph as the trigger can achieve better attack performance. (ii) Effectiveness: Motif-Backdoor reaches the state-of-the-art (SOTA) attack performance in both black-box and defensive scenarios. (iii) Efficiency: based on the graph motif distribution, Motif-Backdoor can quickly obtain an effective trigger structure without target model feedback or subgraph model generation. Extensive experimental results show that Motif-Backdoor realizes the SOTA performance on three popular models and four public datasets compared with five baselines.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.13710")
get_code_for_paper("2210.13710")
have("2210.13710")
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