4 papers
TopFeaRe: Locating Critical State of Adversarial Resilience for Graphs Regarding Topology-Feature Entanglement
Xinxin Fan, Wenxiong Chen, Quanliang Jing +4
Graph adversarial attacks are usually produced from the two perspectives of topology/structure and node feature, both of them represent the paramount characteristics learned by tod…
CAMA: Exploring Collusive Adversarial Attacks in c-MARL
Men Niu, Xinxin Fan, Quanliang Jing +2
Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, etc. Neverthe…
SifterNet: A Generalized and Model-Agnostic Trigger Purification Approach
Shaoye Luo, Xinxin Fan, Quanliang Jing +4
Aiming at resisting backdoor attacks in convolution neural networks and vision Transformer-based large model, this paper proposes a generalized and model-agnostic trigger-purificat…
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium
Xinxin Fan, Wenxiong Chen, Mengfan Li +2
Adversarial attacks to graph analytics are gaining increased attention. To date, two lines of countermeasures have been proposed to resist various graph adversarial attacks from th…