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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 476–500 of 523 papers

TitleStatusHype
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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