バックドア攻撃

Rethinking Backdoor Attacks

Authors: Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry | Published: 2023-07-19
データの隠蔽
バックドア攻撃
ポイズニング

Application of BadNets in Spam Filters

Authors: Swagnik Roychoudhury, Akshaj Kumar Veldanda | Published: 2023-07-18
LSTMモデル性能評価
バックドア攻撃
学習の改善

On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks

Authors: Wenxiao Wang, Soheil Feizi | Published: 2023-06-28
データの起源と変遷
データ汚染検出
バックドア攻撃

Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network

Authors: Fan Liu, Siqi Lai, Yansong Ning, Hao Liu | Published: 2023-06-17
バックドア攻撃
連合学習

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

Authors: Mohamed Amine Ferrag, Othmane Friha, Burak Kantarci, Norbert Tihanyi, Lucas Cordeiro, Merouane Debbah, Djallel Hamouda, Muna Al-Hawawreh, Kim-Kwang Raymond Choo | Published: 2023-06-17 | Updated: 2024-02-08
バックドア攻撃
ポイズニング
連合学習

Extracting Cloud-based Model with Prior Knowledge

Authors: Shiqian Zhao, Kangjie Chen, Meng Hao, Jian Zhang, Guowen Xu, Hongwei Li, Tianwei Zhang | Published: 2023-06-07 | Updated: 2023-06-13
バックドア攻撃
攻撃の評価
敵対的攻撃

Exploring Model Dynamics for Accumulative Poisoning Discovery

Authors: Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han | Published: 2023-06-06
バックドア攻撃
モデルの設計や精度
敵対的攻撃

A Survey on Federated Learning Poisoning Attacks and Defenses

Authors: Junchuan Lianga, Rong Wang, Chaosheng Feng, Chin-Chen Chang | Published: 2023-06-06
バックドア攻撃
ポイズニング
未ターゲット毒性攻撃

Poisoning Network Flow Classifiers

Authors: Giorgio Severi, Simona Boboila, Alina Oprea, John Holodnak, Kendra Kratkiewicz, Jason Matterer | Published: 2023-06-02
バックドア攻撃
ポイズニング
敵対的攻撃

Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning

Authors: Deniz Koyuncu, Alex Gittens, Bülent Yener, Moti Yung | Published: 2023-05-31
バックドア攻撃
敵対的攻撃
欠損データを利用した因果発見