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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 391–400 of 492 papers

TitleStatusHype
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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