M201 類神經正切泛化攻擊模型
摘要:
This is the repo for Neural Tangent Generalization Attacks, Chia-Hung Yuan and Shan-Hung Wu, In Proceedings of ICML 2021.
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說明
github : https://github.com/lionelmessi6410/ntga
This is the repo for Neural Tangent Generalization Attacks, Chia-Hung Yuan and Shan-Hung Wu, In Proceedings of ICML 2021.
We 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. We 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. Please see Results or the main paper for more complete results. We also release the unlearnable MNIST, CIFAR-10, and 2-class ImageNet generated by NTGA, which can be found and downloaded in Unlearnable Datasets, and also launch learning on unlearnable data competitions. The following figures show one clean and the corresponding poisoned examples.
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