4 citations · 4 across the 6 of their papers we have counts for
7 papers
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…
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations
Hoang-Quoc Nguyen-Son, Seira Hidano, Kazuhide Fukushima +2
Adversarial attacks reveal serious flaws in deep learning models. More dangerously, these attacks preserve the original meaning and escape human recognition. Existing methods for d…
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…
SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text
Hoang-Quoc Nguyen-Son, Seira Hidano, Kazuhide Fukushima +1
There are two cases describing how a classifier processes input text, namely, misclassification and correct classification. In terms of misclassified texts, a classifier handles th…
Lattice-based Signcryption with Equality Test in Standard Model
Huy Quoc Le, Dung Hoang Duong, Partha Sarathi Roy +3
A signcryption, which is an integration of a public key encryption and a digital signature, can provide confidentiality and authenticity simultaneously. Additionally, a signcryptio…
Lattice-based public key encryption with equality test supporting flexible authorization in standard model
Dung Hoang Duong, Kazuhide Fukushima, Shinsaku Kiyomoto +3
Public key encryption with equality test (PKEET) supports to check whether two ciphertexts encrypted under different public keys contain the same message or not. PKEET has many int…