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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 151–160 of 523 papers

TitleStatusHype
Backdooring Outlier Detection Methods: A Novel Attack Approach—0
Backdooring Convolutional Neural Networks via Targeted Weight Perturbations—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers—0
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models—0
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection—0
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