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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 231–240 of 523 papers

TitleStatusHype
Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks—0
MEGen: Generative Backdoor in Large Language Models via Model Editing—0
A Disguised Wolf Is More Harmful Than a Toothless Tiger: Adaptive Malicious Code Injection Backdoor Attack Leveraging User Behavior as Triggers—0
Diff-Cleanse: Identifying and Mitigating Backdoor Attacks in Diffusion ModelsCode0
DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers—0
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Krait: A Backdoor Attack Against Graph Prompt Tuning—0
Backdoor Attacks against Image-to-Image Networks—0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
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