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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
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
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning—0
Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained models—0
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering—0
Handcrafted Backdoors in Deep Neural Networks—0
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios—0
Heterogeneous Graph Backdoor Attack—0
Hidden Backdoor Attack against Deep Learning-Based Wireless Signal Modulation Classifiers—0
Hidden Backdoor Attack against Semantic Segmentation Models—0
HoneypotNet: Backdoor Attacks Against Model Extraction—0
Impart: An Imperceptible and Effective Label-Specific Backdoor Attack—0
Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks—0
Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control—0
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving—0
Inferring Properties of Graph Neural Networks—0
Injecting Bias into Text Classification Models using Backdoor Attacks—0
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain—0
Invisible Backdoor Attack Through Singular Value Decomposition—0
Invisible Threats: Backdoor Attack in OCR Systems—0
Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?—0
Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection—0
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers—0
Krait: A Backdoor Attack Against Graph Prompt Tuning—0
LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm—0
Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks—0
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