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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 461–470 of 492 papers

TitleStatusHype
Data Poisoning Attacks on Stochastic Bandits—0
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances—0
Data Poisoning Attack against Knowledge Graph Embedding—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Data Poisoning against Differentially-Private Learners: Attacks and Defenses—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
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
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