14 citations · 45 across the 14 of their papers we have counts for
6 papers · 1 filter
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning
Hiroya Kato, Kento Hasegawa, Seira Hidano +1
Graph Contrastive Learning (GCL) is unsupervised graph representation learning that can obtain useful representation of unknown nodes. The node representation can be utilized as fe…
Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Jung-Woo Chang, Ke Sun, Nasimeh Heydaribeni +3
Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems. Althou…
R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training
Kento Hasegawa, Seira Hidano, Kohei Nozawa +2
Hardware Trojans (HTs) have become a serious problem, and extermination of them is strongly required for enhancing the security and safety of integrated circuits. An effective solu…
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
Seira Hidano, Takao Murakami
Differentially private GNNs (Graph Neural Networks) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In pa…
Node-wise Hardware Trojan Detection Based on Graph Learning
Kento Hasegawa, Kazuki Yamashita, Seira Hidano +3
In the fourth industrial revolution, securing the protection of the supply chain has become an ever-growing concern. One such cyber threat is a hardware Trojan (HT), a malicious mo…
TransMIA: Membership Inference Attacks Using Transfer Shadow Training
Seira Hidano, Takao Murakami, Yusuke Kawamoto
Transfer learning has been widely studied and gained increasing popularity to improve the accuracy of machine learning models by transferring some knowledge acquired in different t…