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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 376–400 of 492 papers

TitleStatusHype
Adversarial Vulnerability of Active Transfer Learning—0
Data Poisoning Attacks to Deep Learning Based Recommender Systems—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Sself: Robust Federated Learning against Stragglers and Adversaries—0
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks—0
Federated Unlearning—0
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses—0
Exacerbating Algorithmic Bias through Fairness AttacksCode0
Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers—0
Mitigating the Impact of Adversarial Attacks in Very Deep Networks—0
Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Lethean Attack: An Online Data Poisoning TechniqueCode0
Dimensionality reduction, regularization, and generalization in overparameterized regressionsCode0
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy TradeoffCode1
Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems—0
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning—0
Model-Agnostic Explanations using Minimal Forcing Subsets—0
Concealed Data Poisoning Attacks on NLP Models—0
GFL: A Decentralized Federated Learning Framework Based On Blockchain—0
VenoMave: Targeted Poisoning Against Speech RecognitionCode0
Sniper GMMs: Structured Gaussian mixtures poison ML on large n small p data with high efficacy—0
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
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