@inproceedings{035f7dbcb3b34fd88783439dc1650553,
title = "Backdoors on Manifold Learning",
abstract = "Recently, attackers have targeted machine learning systems, introducing various attacks. The backdoor attack is popular in this field and is usually realized through data poisoning. To the best of our knowledge, we are the first to investigate whether the backdoor attacks remain effective when manifold learning algorithms are applied to the poisoned dataset. We conducted our experiments using two manifold learning techniques (Autoencoder and UMAP) on two benchmark datasets (MNIST and CIFAR10) and two backdoor strategies (clean and dirty label). We performed an array of experiments using different parameters, finding that we could reach an attack success rate of 95% and 75% even after reducing our data to two dimensions using Autoencoders and UMAP, respectively.",
keywords = "autoencoders, backdoor attacks, manifold learning, umap",
author = "Christina Kreza and Stefanos Koffas and Behrad Tajalli and Mauro Conti and Stjepan Picek",
year = "2024",
doi = "10.1145/3649403.3656484",
language = "English",
series = "WiseML 2024 - Proceedings of the 2024 ACM Workshop on Wireless Security and Machine Learning",
publisher = "ACM",
pages = "1--7",
booktitle = "WiseML 2024 - Proceedings of the 2024 ACM Workshop on Wireless Security and Machine Learning",
address = "United States",
note = "2024 ACM Workshop on Wireless Security and Machine Learning, WiseML 2024 ; Conference date: 30-05-2024",
}