AIセキュリティポータルbot

Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation

Authors: Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer, Emil C. Lupu | Published: 2020-02-28 | Updated: 2020-06-20
ハイパーパラメータ最適化
ポイズニング
ロバスト性評価

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

Authors: Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh | Published: 2020-02-28 | Updated: 2020-10-26
ロバスト性評価
脆弱性分析
防御手法

Certified Defense to Image Transformations via Randomized Smoothing

Authors: Marc Fischer, Maximilian Baader, Martin Vechev | Published: 2020-02-27 | Updated: 2021-08-25
ロバスト性評価
計算の整合性
防御手法

TSS: Transformation-Specific Smoothing for Robustness Certification

Authors: Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, Bo Li | Published: 2020-02-27 | Updated: 2021-11-16
ロバスト性評価
変換の影響
統計的手法

Heterogeneous Graph Neural Networks for Malicious Account Detection

Authors: Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song | Published: 2020-02-27
GNN
グラフプライバシー
機械学習手法

Entangled Watermarks as a Defense against Model Extraction

Authors: Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran, Nicolas Papernot | Published: 2020-02-27 | Updated: 2021-02-19
DNN IP保護手法
ロバスト性評価
防御手法

FMix: Enhancing Mixed Sample Data Augmentation

Authors: Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare | Published: 2020-02-27 | Updated: 2021-02-28
トレーニング手法
ロバスト性評価
性能評価

Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy

Authors: Aditya Saligrama, Guillaume Leclerc | Published: 2020-02-26
ロバスト性評価
性能評価
敵対的訓練

IoT Device Identification Using Deep Learning

Authors: Jaidip Kotak, Yuval Elovici | Published: 2020-02-25
データ管理システム
性能評価
機械学習手法

Gödel’s Sentence Is An Adversarial Example But Unsolvable

Authors: Xiaodong Qi, Lansheng Han | Published: 2020-02-25
敵対的サンプル
敵対的訓練
脆弱性予測