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Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

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Showing 176–200 of 492 papers

TitleStatusHype
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Cut the Deadwood Out: Post-Training Model Purification with Selective Module Substitution—0
Concealed Data Poisoning Attacks on NLP Models—0
Backdoor Attack and Defense for Deep Regression—0
ControlNET: A Firewall for RAG-based LLM System—0
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Computation and Data Efficient Backdoor Attacks—0
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Attacks on the neural network and defense methods—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Clean Label Attacks against SLU Systems—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
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