Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data
Authors: Jacson Rodrigues Correia-Silva, Rodrigo F. Berriel, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos | Published: 2018-06-14
ポイズニング
Adversarial Regression with Multiple Learners
Authors: Liang Tong, Sixie Yu, Scott Alfeld, Yevgeniy Vorobeychik | Published: 2018-06-06
Adversarial Attacks on Neural Networks for Graph Data
Authors: Daniel Zügner, Amir Akbarnejad, Stephan Günnemann | Published: 2018-05-21 | Updated: 2021-12-09
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
Authors: Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang, Bernt Schiele, Mario Fritz | Published: 2018-05-15 | Updated: 2020-09-13
Is feature selection secure against training data poisoning?
Authors: Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli | Published: 2018-04-21
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Authors: Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein | Published: 2018-04-03 | Updated: 2018-11-10
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Authors: Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li | Published: 2018-04-01 | Updated: 2021-09-28
Adversarial Defense based on Structure-to-Signal Autoencoders
Authors: Joachim Folz, Sebastian Palacio, Joern Hees, Damian Borth, Andreas Dengel | Published: 2018-03-21
The Hidden Vulnerability of Distributed Learning in Byzantium
Authors: El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault | Published: 2018-02-22 | Updated: 2018-07-17
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning
Authors: Christopher Frederickson, Michael Moore, Glenn Dawson, Robi Polikar | Published: 2018-02-20