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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 81–90 of 523 papers

TitleStatusHype
BadSFL: Backdoor Attack against Scaffold Federated Learning—0
LoBAM: LoRA-Based Backdoor Attack on Model Merging—0
Memory Backdoor Attacks on Neural Networks—0
AnywhereDoor: Multi-Target Backdoor Attacks on Object DetectionCode0
When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations—0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization—0
TrojanRobot: Physical-World Backdoor Attacks Against VLM-based Robotic Manipulation—0
Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models—0
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning—0
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