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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 391–400 of 523 papers

TitleStatusHype
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks—0
BadScan: An Architectural Backdoor Attack on Visual State Space Models—0
BadSFL: Backdoor Attack against Scaffold Federated Learning—0
EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events—0
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors—0
BadVFL: Backdoor Attacks in Vertical Federated Learning—0
BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization—0
BATT: Backdoor Attack with Transformation-based Triggers—0
BDMMT: Backdoor Sample Detection for Language Models through Model Mutation Testing—0
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