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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 451–500 of 523 papers

TitleStatusHype
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy BreachesCode0
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers—0
False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger—0
Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks—0
Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
Neighboring Backdoor Attacks on Graph Convolutional Network—0
Defending against Backdoor Attacks in Natural Language Generation—0
Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints—0
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks—0
Backdoor Attack with Imperceptible Input and Latent Modification—0
Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated LearningCode0
Backdoor Attack through Frequency DomainCode0
DBIA: Data-free Backdoor Injection Attack against Transformer NetworksCode0
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences—0
Enhancing Backdoor Attacks with Multi-Level MMD RegularizationCode0
Backdoor Pre-trained Models Can Transfer to AllCode0
Widen The Backdoor To Let More Attackers In—0
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained models—0
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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