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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 141–150 of 523 papers

TitleStatusHype
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated LearningCode0
Attack On Prompt: Backdoor Attack in Prompt-Based Continual Learning—0
Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift—0
CBPF: Filtering Poisoned Data Based on Composite Backdoor Attack—0
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation—0
Backdooring Bias into Text-to-Image ModelsCode0
Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach—0
Federated Learning with Flexible Architectures—0
An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong DetectionCode2
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
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