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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 451–475 of 523 papers

TitleStatusHype
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
Dynamic Backdoor Attacks Against Machine Learning Models—0
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks—0
Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats—0
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models—0
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation—0
EmoAttack: Utilizing Emotional Voice Conversion for Speech Backdoor Attacks on Deep Speech Classification Models—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers—0
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure—0
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack—0
Evil from Within: Machine Learning Backdoors through Hardware Trojans—0
Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense—0
Explainability-based Backdoor Attacks Against Graph Neural Networks—0
Exploring Backdoor Attack and Defense for LLM-empowered Recommendations—0
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry—0
Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion—0
False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger—0
Feature Grinding: Efficient Backdoor Sanitation in Deep Neural Networks—0
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
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