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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 281–290 of 492 papers

TitleStatusHype
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch SamplingCode0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
PORE: Provably Robust Recommender Systems against Data Poisoning AttacksCode0
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning—0
Naive Bayes Classifiers over Missing Data: Decision and PoisoningCode0
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning AttacksCode0
WW-FL: Secure and Private Large-Scale Federated Learning—0
QTrojan: A Circuit Backdoor Against Quantum Neural Networks—0
Explainable Label-flipping Attacks on Human Emotion Assessment System—0
Training-free Lexical Backdoor Attacks on Language ModelsCode0
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