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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
Subpopulation Data Poisoning AttacksCode0
On Adversarial Bias and the Robustness of Fair Machine LearningCode0
Robust Variational Autoencoder for Tabular Data with Beta Divergence—0
Online Data Poisoning Attacks—0
Attacking Black-box Recommendations via Copying Cross-domain User ProfilesCode0
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm—0
Depth-2 Neural Networks Under a Data-Poisoning AttackCode0
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers—0
Data Poisoning Attacks on Federated Machine Learning—0
Practical Data Poisoning Attack against Next-Item Recommendation—0
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
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
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
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
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