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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 471–480 of 523 papers

TitleStatusHype
Federated Learning with Flexible Architectures—0
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition—0
Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras—0
FRIB: Low-poisoning Rate Invisible Backdoor Attack based on Feature Repair—0
FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning—0
Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained models—0
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering—0
Handcrafted Backdoors in Deep Neural Networks—0
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