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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 301–325 of 492 papers

TitleStatusHype
Breaking Fair Binary Classification with Optimal Flipping Attacks—0
Machine Learning Security against Data Poisoning: Are We There Yet?Code0
Robustly-reliable learners under poisoning attacks—0
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation—0
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy—0
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
Redactor: A Data-centric and Individualized Defense Against Inference Attacks—0
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite AggregationCode0
Towards Multi-Objective Statistically Fair Federated Learning—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of RobustnessCode0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning—0
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks—0
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural NetworksCode1
Poisoning Knowledge Graph Embeddings via Relation Inference PatternsCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
Get a Model! Model Hijacking Attack Against Machine Learning Models—0
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Mitigating Data Poisoning in Text Classification with Differential Privacy—0
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