In this work, we try to crack the unlearnable dataset which proposed by Neural Tangent Generalization Attacks (NTGA) ICML'21

Authors of NTGA propose the generalization attack, a new direction for poisoning attacks, where an attacker aims to modify training data in order to spoil the training process such that a trained network lacks generalizability. They devise Neural Tangent Generalization Attack (NTGA), a first efficient work enabling clean-label, black-box generalization attacks against Deep Neural Networks.

NTGA declines the generalization ability sharply, i.e. 99% -> 15%, 92% -> 33%, 99% -> 72% on MNIST, CIFAR10 and 2- class ImageNet, respectively.