SOTAVerified

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 211–220 of 523 papers

TitleStatusHype
Backdoor Attack on Vertical Federated Graph Neural Network Learning—0
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models—0
"No Matter What You Do": Purifying GNN Models via Backdoor UnlearningCode0
Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats—0
BadHMP: Backdoor Attack against Human Motion Prediction—0
Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery—0
TrojVLM: Backdoor Attack Against Vision Language Models—0
Weak-to-Strong Backdoor Attack for Large Language Models—0
Claim-Guided Textual Backdoor Attack for Practical ApplicationsCode0
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