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Backdoor Attack

Backdoor attacks inject maliciously constructed data into a training set so that, at test time, the trained model misclassifies inputs patched with a backdoor trigger as an adversarially-desired target class.

Papers

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Showing 81–90 of 523 papers

Title	Date	Tasks	Status	Hype
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers	Oct 16, 2020	Backdoor AttackBIG-bench Machine Learning	CodeCode Available	1
Backdoor Attacks to Graph Neural Networks	Jun 19, 2020	Backdoor AttackGeneral Classification	CodeCode Available	1
FedDefender: Backdoor Attack Defense in Federated Learning	Jul 2, 2023	Backdoor AttackData Poisoning	CodeCode Available	1
Few-Shot Backdoor Attacks on Visual Object Tracking	Jan 31, 2022	Autonomous DrivingBackdoor Attack	CodeCode Available	1
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning	Oct 23, 2022	Backdoor Attackbackdoor defense	CodeCode Available	1
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge	Dec 15, 2023	Backdoor AttackData Poisoning	CodeCode Available	1
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models	Mar 29, 2021	Backdoor AttackData Poisoning	CodeCode Available	1
Backdoor Attack with Sparse and Invisible Trigger	May 11, 2023	Backdoor Attack	CodeCode Available	1
Hidden Trigger Backdoor Attacks	Sep 30, 2019	Backdoor Attackimage-classification	CodeCode Available	1
Defending against Backdoors in Federated Learning with Robust Learning Rate	Jul 7, 2020	Backdoor AttackFederated Learning	CodeCode Available	1

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