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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 171–180 of 523 papers

TitleStatusHype
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors—0
HoneypotNet: Backdoor Attacks Against Model Extraction—0
Stealthy Backdoor Attack to Real-world Models in Android Apps—0
Injecting Bias into Text Classification Models using Backdoor Attacks—0
Double Landmines: Invisible Textual Backdoor Attacks based on Dual-Trigger—0
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning—0
A Backdoor Attack Scheme with Invisible Triggers Based on Model Architecture Modification—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion ModelsCode0
Stealthy and Robust Backdoor Attack against 3D Point Clouds through Additional Point Features—0
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