SOTAVerified

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 476–500 of 523 papers

TitleStatusHype
Feature Grinding: Efficient Backdoor Sanitation in Deep Neural Networks—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
FooBaR: Fault Fooling Backdoor Attack on Neural Network TrainingCode0
BFClass: A Backdoor-free Text Classification Framework—0
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain—0
Backdoor Attack and Defense for Deep Regression—0
Excess Capacity and Backdoor PoisoningCode0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box setting—0
BadNL: Backdoor Attacks Against NLP Models—0
Handcrafted Backdoors in Deep Neural Networks—0
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations—0
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds—0
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning—0
A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification—0
Stealthy Backdoors as Compression ArtifactsCode0
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World—0
Explainability-based Backdoor Attacks Against Graph Neural Networks—0
Backdoor Attack in the Physical World—0
PointBA: Towards Backdoor Attacks in 3D Point Cloud—0
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry—0
Hidden Backdoor Attack against Semantic Segmentation Models—0
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models—0
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection—0
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models—0
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