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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 471–480 of 492 papers

TitleStatusHype
Online Data Poisoning Attack—0
TrojDRL: Trojan Attacks on Deep Reinforcement Learning AgentsCode0
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks—0
Spectrum Data Poisoning with Adversarial Deep Learning—0
Reaching Data Confidentiality and Model Accountability on the CalTrain—0
An Optimal Control View of Adversarial Machine Learning—0
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks—0
Spectral Signatures in Backdoor AttacksCode0
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters—0
Data Poisoning Attack against Unsupervised Node Embedding Methods—0
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