* External authors




Minimum Topology Attacks for Graph Neural Networks

Mengmei Zhang*

Xiao Wang*

Chuan Shi*

Lingjuan Lyu

Tianchi Yang*

Junping Du*

* External authors

WWW 2023



With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received increasing attention. Although many attack methods have been proposed, they mainly focus on fixed-budget attacks, aiming at finding the most adversarial perturbations within a fixed budget for target node. However, considering the varied robustness of each node, there is an inevitable dilemma caused by the fixed budget, i.e., no successful perturbation is found when the budget is relatively small, while if it is too large, the yielding redundant perturbations will hurt the invisibility. To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node. To this end, we propose an attack model, named MiBTack, based on a dynamic projected gradient descent algorithm, which can effectively solve the involving non-convex constraint optimization on discrete topology. Extensive results on three GNNs and four real-world datasets show that MiBTack can successfully lead all target nodes misclassified with the minimum perturbation edges. Moreover, the obtained minimum budget can be used to measure node robustness, so we can explore the relationships of robustness, topology, and uncertainty for nodes, which is beyond what the current fixed-budget topology attacks can offer.

Related Publications

FedMef: Towards Memory-efficient Federated Dynamic Pruning

CVPR, 2024
Hong Huang, Weiming Zhuang, Chen Chen, Lingjuan Lyu

Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the high demand for computation and memory resources for training deep learning models. Neural netw…

DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models

ICLR, 2024
Zhenting Wang, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas*, Shiqing Ma*

Recent text-to-image diffusion models have shown surprising performance in generating high-quality images. However, concerns have arisen regarding the unauthorized data usage during the training or fine-tuning process. One example is when a model trainer collects a set of im…

FedWon: Triumphing Multi-domain Federated Learning Without Normalization

ICLR, 2024
Weiming Zhuang, Lingjuan Lyu

Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients. Nevertheless, FL encounters challenges due to non-independent and identically distributed (non-i.i.d) data, leading to potential performance degradation and hindered c…


Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.