SOTAVerified

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
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers—0
Fooling Partial Dependence via Data PoisoningCode0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Incompatibility Clustering as a Defense Against Backdoor Poisoning AttacksCode0
Influence Based Defense Against Data Poisoning Attacks in Online Learning—0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning—0
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks—0
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?Code0
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study—0
Robust learning under clean-label attack—0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models—0
Data Poisoning Attacks and Defenses to Crowdsourcing Systems—0
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release—0
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification—0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models—0
Property Inference From Poisoning—0
Adversarial Vulnerability of Active Transfer Learning—0
Data Poisoning Attacks to Deep Learning Based Recommender Systems—0
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Sself: Robust Federated Learning against Stragglers and Adversaries—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Federated Unlearning—0
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