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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 426–450 of 492 papers

TitleStatusHype
Data Poisoning Attacks on Federated Machine Learning—0
Practical Data Poisoning Attack against Next-Item Recommendation—0
MetaPoison: Practical General-purpose Clean-label Data PoisoningCode1
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks—0
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM—0
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation—0
Defending against Backdoor Attack on Deep Neural Networks—0
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient ShapingCode1
FR-Train: A Mutual Information-Based Approach to Fair and Robust TrainingCode1
Influence Function based Data Poisoning Attacks to Top-N Recommender Systems—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Radioactive data: tracing through trainingCode1
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
Deep Probabilistic Models to Detect Data Poisoning Attacks—0
Proving Data-Poisoning Robustness in Decision Trees—0
Data Poisoning Attacks on Neighborhood-based Recommender Systems—0
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning—0
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic—0
Penalty Method for Inversion-Free Deep Bilevel OptimizationCode1
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
Shapley Homology: Topological Analysis of Sample Influence for Neural Networks—0
Detecting AI Trojans Using Meta Neural AnalysisCode0
Deep k-NN Defense against Clean-label Data Poisoning AttacksCode0
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