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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 301–350 of 523 papers

TitleStatusHype
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models—0
VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion—0
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks—0
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks—0
Weak-to-Strong Backdoor Attack for Large Language Models—0
When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations—0
Widen The Backdoor To Let More Attackers In—0
You Are Catching My Attention: Are Vision Transformers Bad Learners Under Backdoor Attacks?—0
DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers—0
Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models—0
Data-centric NLP Backdoor Defense from the Lens of Memorization—0
SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models—0
A4O: All Trigger for One sample—0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
A Backdoor Attack Scheme with Invisible Triggers Based on Model Architecture Modification—0
A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
A clean-label graph backdoor attack method in node classification task—0
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning—0
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks—0
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
A Disguised Wolf Is More Harmful Than a Toothless Tiger: Adaptive Malicious Code Injection Backdoor Attack Leveraging User Behavior as Triggers—0
A Dual-Purpose Framework for Backdoor Defense and Backdoor Amplification in Diffusion Models—0
A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification—0
An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers—0
An Invisible Backdoor Attack Based On Semantic Feature—0
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences—0
Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning—0
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks—0
Are You Copying My Prompt? Protecting the Copyright of Vision Prompt for VPaaS via Watermark—0
A Robust Attack: Displacement Backdoor Attack—0
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks—0
A semantic backdoor attack against Graph Convolutional Networks—0
AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration—0
A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning—0
A Survey on Backdoor Attack and Defense in Natural Language Processing—0
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection—0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models—0
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models—0
Backdoor Attack against NLP models with Robustness-Aware Perturbation defense—0
Backdoor Attack Against Vision Transformers via Attention Gradient-Based Image Erosion—0
Backdoor Attack and Defense for Deep Regression—0
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis—0
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