15 citations · 52 across the 14 of their papers we have counts for
7 papers · 1 filter
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis
Yuwei Sun, Hideya Ochiai, Jun Sakuma
Model poisoning attacks on federated learning (FL) intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such comprom…
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation
Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto +1
We aim to explain a black-box classifier with the form: `data X is classified as class Y because X \textit{has} A, B and \textit{does not have} C' in which A, B, and C are high-lev…
Application of Adversarial Examples to Physical ECG Signals
Taiga Ono, Takeshi Sugawara, Jun Sakuma +1
This work aims to assess the reality and feasibility of the adversarial attack against cardiac diagnosis system powered by machine learning algorithms. To this end, we introduce ad…
AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment
Rei Sato, Jun Sakuma, Youhei Akimoto
Neural architecture search (NAS) is an approach for automatically designing a neural network architecture without human effort or expert knowledge. However, the high computational…
Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs
Thien Q. Tran, Jun Sakuma
Search engine logs have a great potential in tracking and predicting outbreaks of infectious disease. More precisely, one can use the search volume of some search terms to predict…
Generate (non-software) Bugs to Fool Classifiers
Hiromu Yakura, Youhei Akimoto, Jun Sakuma
In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. O…