文献データベース

An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning

Authors: Xue Yang, Yan Feng, Weijun Fang, Jun Shao, Xiaohu Tang, Shu-Tao Xia, Rongxing Lu | Published: 2020-02-23 | Updated: 2021-08-15
プライバシー保護メカニズム
連合学習
防御手法

Neuron Shapley: Discovering the Responsible Neurons

Authors: Amirata Ghorbani, James Zou | Published: 2020-02-23 | Updated: 2020-11-13
性能評価
特徴重要度分析
脆弱性予測

Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks

Authors: Kirthi Shankar Sivamani, Rajeev Sahay, Aly El Gamal | Published: 2020-02-22
性能評価
敵対的訓練
防御手法

Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers

Authors: Chen Zhu, Renkun Ni, Ping-yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein | Published: 2020-02-22
ロバスト性評価
最適化問題
正則化

Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples

Authors: Guanxiong Liu, Issa Khalil, Abdallah Khreishah | Published: 2020-02-22 | Updated: 2020-02-27
性能評価
敵対的サンプル
敵対的訓練

Adversarial Attacks on Machine Learning Systems for High-Frequency Trading

Authors: Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein | Published: 2020-02-21 | Updated: 2021-10-29
取引生成手法
敵対的サンプル
脆弱性予測

Robustness from Simple Classifiers

Authors: Sharon Qian, Dimitris Kalimeris, Gal Kaplun, Yaron Singer | Published: 2020-02-21
ロバスト性評価
敵対的訓練
防御手法

Adversarial Detection and Correction by Matching Prediction Distributions

Authors: Giovanni Vacanti, Arnaud Van Looveren | Published: 2020-02-21
アドバイス提供
敵対的訓練
防御手法

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework

Authors: Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu | Published: 2020-02-21 | Updated: 2020-10-20
ロバスト性評価
最適化問題
防御手法

Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning

Authors: Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, I-Te Danny Hung, Chin-Hui Lee, Xiaoli Ma | Published: 2020-02-20
報酬メカニズム設計
脆弱性予測
防御手法