21 citations · 22 across the 4 of their papers we have counts for
4 papers
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
Kirill Lukyanov, Mikhail Drobyshevskiy, Georgii Sazonov +2
The growing need for Trusted AI (TAI) highlights the importance of interpretability and robustness in machine learning models. However, many existing tools overlook graph data and…
Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples
Kirill Lukyanov, Andrew Perminov, Denis Turdakov +1
The vulnerability of artificial neural networks to adversarial perturbations in the black-box setting is widely studied in the literature. The majority of attack methods to constru…
Graph Neural Network for Crawling Target Nodes in Social Networks
Kirill Lukyanov, Mikhail Drobyshevskiy, Danil Shaikhelislamov +1
Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of c…
Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process
Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov +2
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of th…