Sniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning

Federated Learning (FL) enables collaborative training of Deep Learning (DL) models where the data is retained locally. Like DL, FL has severe security weaknesses that the attackers can exploit, e.g., model inversion and backdoor attacks. Model inversion attacks reconstruct the data from the trainin...

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Bibliographic Details
Published in2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) pp. 377 - 391
Main Authors Abad, Gorka, Paguada, Servio, Ersoy, Oguzhan, Picek, Stjepan, Ramirez-Duran, Victor Julio, Urbieta, Aitor
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2023
Subjects
Backdoor attack
Collaboration
Deep learning
Federated learning
Neural networks
Threat modeling
Toxicology
Training
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Summary:Federated Learning (FL) enables collaborative training of Deep Learning (DL) models where the data is retained locally. Like DL, FL has severe security weaknesses that the attackers can exploit, e.g., model inversion and backdoor attacks. Model inversion attacks reconstruct the data from the training datasets, whereas backdoors misclassify only classes containing specific properties, e.g., a pixel pattern. Backdoors are prominent in FL and aim to poison every client model, while model inversion attacks can target even a single client. This paper introduces a novel technique to allow backdoor attacks to be client-targeted, compromising a single client while the rest remain unchanged. The attack takes advantage of state-of-the-art model inversion and backdoor attacks. Precisely, we leverage a Generative Adversarial Network to perform the model inversion. Afterward, we shadow-train the FL network, in which, using a Siamese Neural Network, we can identify, target, and backdoor the victim's model. Our attack has been validated using the MNIST, F-MNIST, EMNIST, and CIFAR-100 datasets under different settings-achieving up to 99 % accuracy on both source (clean) and target (backdoor) classes and against state-of-the-art defenses, e.g., Neural Cleanse, opening a novel threat model to be considered in the future.
DOI:10.1109/SaTML54575.2023.00033