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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 101–125 of 492 papers

TitleStatusHype
Atlas: A Framework for ML Lifecycle Provenance & Transparency—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Clean Label Attacks against SLU Systems—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Computation and Data Efficient Backdoor Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Data Poisoning Attacks on Factorization-Based Collaborative Filtering—0
ControlNET: A Firewall for RAG-based LLM System—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks—0
Concealed Data Poisoning Attacks on NLP Models—0
Cut the Deadwood Out: Post-Training Model Purification with Selective Module Substitution—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
Data-Dependent Stability Analysis of Adversarial Training—0
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study—0
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
A Robust Attack: Displacement Backdoor Attack—0
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