Optimizing Adversarial Attacks on Graph Neural Networks via Honey Badger Energy Valley Optimization
Keywords:
Energy valley optimization, Honey Badger Optimization Algorithm, Graph Neural Network, Energy Honey Badger OptimizationAbstract
In recent years, Graph Neural Networks (GNN) has gained considerable attention due to the practical importance of graph structure data in graph representation learning. It is most commonly utilized in fraud detection, privacy-inference attacks, completion of knowledge graphs, item recommendation, and so on. The GNN is highly vulnerable to adversarial attacks, which affect the reliability of the system, reduce the accuracy of prediction on test data, and increase the loss function of training data. However, the existing approaches utilized to reduce the impacts of adversarial attacks on GNN focus only on highly linked training process. Thus, a GNN_Attacker model is designed in this research for the generation of adversarial attacks in GNN. The binary image is allowed for graph construction and the adversarial attacks are generated in the constructed graph using GNN. Here, the Energy Honey Badger Optimization (EHBO) is introduced for the generation of training samples and GNN is again utilized for testing the generated adversarial attacks. Moreover, the adversarial attack generation performance of GNN_Attacker is validated. It demonstrates that the GNN_Attacker attained superior performance with maximum visual similarity, classification accuracy, and attack success rate of 90.77%, 94.68%, and 96.54% respectively.
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