Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph
Learning Models

Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models

12 February 2020

Jie Chen

Papers citing "Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models"

7 / 7 papers shown

Title
RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN Huy Phan Cong Shi Yi Xie Tian-Di Zhang Zhuohang Li Tianming Zhao Jian-Dong Liu Yan Wang Ying Chen Bo Yuan AAML 15 6 0 22 Aug 2022
Transferable Graph Backdoor Attack Shuiqiao Yang Bao Gia Doan Paul Montague O. Vel Tamas Abraham S. Çamtepe D. Ranasinghe S. Kanhere AAML 29 36 0 21 Jun 2022
Strategic Classification with Graph Neural Networks Itay Eilat Ben Finkelshtein Chaim Baskin Nir Rosenfeld 27 11 0 31 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 41 104 0 16 May 2022
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees Binghui Wang Youqin Li Pan Zhou AAML 20 13 0 07 May 2022
GraphAttacker: A General Multi-Task GraphAttack Framework Jinyin Chen Dunjie Zhang Zhaoyan Ming Kejie Huang Wenrong Jiang Chen Cui AAML 31 14 0 18 Jan 2021
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Xiang Zhang Marinka Zitnik AAML 16 287 0 15 Jun 2020