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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 351–400 of 492 papers

TitleStatusHype
On the Effectiveness of Poisoning against Unsupervised Domain Adaptation—0
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—0
Gradient-based Data Subversion Attack Against Binary Classifiers—0
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers—0
A Gradient Method for Multilevel Optimization—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
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP ModelsCode1
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
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data AugmentationsCode1
Robust learning under clean-label attack—0
What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data PoisoningCode1
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
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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