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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 401–450 of 523 papers

TitleStatusHype
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
Narcissus: A Practical Clean-Label Backdoor Attack with Limited InformationCode1
Backdoor Attack against NLP models with Robustness-Aware Perturbation defense—0
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning—0
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error AnalysisCode0
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks—0
Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks—0
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving—0
Under-confidence Backdoors Are Resilient and Stealthy BackdoorsCode0
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy BreachesCode0
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias—0
Training with More Confidence: Mitigating Injected and Natural Backdoors During TrainingCode1
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers—0
False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger—0
Few-Shot Backdoor Attacks on Visual Object TrackingCode1
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
FIBA: Frequency-Injection based Backdoor Attack in Medical Image AnalysisCode1
Backdoor Attack with Imperceptible Input and Latent Modification—0
Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated LearningCode0
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural NetworksCode1
DBIA: Data-free Backdoor Injection Attack against Transformer NetworksCode0
Backdoor Attack through Frequency DomainCode0
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences—0
Triggerless Backdoor Attack for NLP Tasks with Clean LabelsCode1
Enhancing Backdoor Attacks with Multi-Level MMD RegularizationCode0
Backdoor Pre-trained Models Can Transfer to AllCode0
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial OutcomesCode1
Anti-Distillation Backdoor Attacks: Backdoors Can Really Survive in Knowledge DistillationCode1
Anti-Backdoor Learning: Training Clean Models on Poisoned DataCode1
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
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
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Feature Grinding: Efficient Backdoor Sanitation in Deep Neural Networks—0
Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained 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
FooBaR: Fault Fooling Backdoor Attack on Neural Network TrainingCode0
BFClass: A Backdoor-free Text Classification Framework—0
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Backdoor Attack on Hash-based Image Retrieval via Clean-label Data PoisoningCode1
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