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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 251–260 of 523 papers

TitleStatusHype
Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach—0
Federated Learning with Flexible Architectures—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Generalization Bound and New Algorithm for Clean-Label Backdoor AttackCode0
DiffPhysBA: Diffusion-based Physical Backdoor Attack against Person Re-Identification in Real-World—0
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents—0
Towards Unified Robustness Against Both Backdoor and Adversarial AttacksCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation ModelsCode0
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