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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

Showing 76100 of 523 papers

TitleStatusHype
BadPrompt: Backdoor Attacks on Continuous PromptsCode1
Backdoor Attacks on Crowd CountingCode1
Invisible Backdoor Attack against Self-supervised LearningCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Backdoor Attacks on Self-Supervised LearningCode1
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP ModelsCode1
Backdoor Attacks to Graph Neural NetworksCode1
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning ModelsCode1
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive LearningCode1
CL-Attack: Textual Backdoor Attacks via Cross-Lingual TriggersCode1
Mask-based Invisible Backdoor Attacks on Object DetectionCode1
Hidden Trigger Backdoor AttacksCode1
Backdoor Attack with Sparse and Invisible TriggerCode1
Composite Backdoor Attacks Against Large Language ModelsCode1
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-PartitioningCode1
Backdoor Defense via Deconfounded Representation LearningCode1
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision TransformersCode1
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision TransfomersCode1
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
Defending Against Backdoor Attacks in Natural Language GenerationCode1
PoisonPrompt: Backdoor Attack on Prompt-based Large Language ModelsCode1
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free CasesCode1
An Embarrassingly Simple Backdoor Attack on Self-supervised LearningCode1
Poison Ink: Robust and Invisible Backdoor AttackCode1
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