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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 151–200 of 523 papers

TitleStatusHype
Backdooring Outlier Detection Methods: A Novel Attack Approach—0
Backdooring Convolutional Neural Networks via Targeted Weight Perturbations—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers—0
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation—0
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models—0
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection—0
Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers—0
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure—0
Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense—0
Backdoor Detection through Replicated Execution of Outsourced Training—0
A Survey on Backdoor Attack and Defense in Natural Language Processing—0
Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats—0
A clean-label graph backdoor attack method in node classification task—0
Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks—0
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks—0
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models—0
AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration—0
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning—0
Dynamic Backdoor Attacks Against Machine Learning Models—0
CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks—0
Backdoor Attack with Mode Mixture Latent Modification—0
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning—0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
DarkMind: Latent Chain-of-Thought Backdoor in Customized LLMs—0
A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning—0
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
Contributor-Aware Defenses Against Adversarial Backdoor Attacks—0
DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data—0
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias—0
Backdoor Attack with Imperceptible Input and Latent Modification—0
Deep Learning Backdoors—0
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection—0
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer—0
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints—0
Defending against Backdoor Attack on Deep Neural Networks—0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability—0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Defending against Backdoor Attacks in Natural Language Generation—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System—0
A semantic backdoor attack against Graph Convolutional Networks—0
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