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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 126–150 of 492 papers

TitleStatusHype
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm—0
A Robust Attack: Displacement Backdoor Attack—0
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Approaching the Harm of Gradient Attacks While Only Flipping Labels—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Breaking Fair Binary Classification with Optimal Flipping Attacks—0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data—0
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
An Optimal Control View of Adversarial Machine Learning—0
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
An Investigation of Data Poisoning Defenses for Online Learning—0
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