arXiv (All)
open access
Empirical Evaluation of Data Poisoning Attacks in Supervised Learning
Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and Ra
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Evasion Attacks: How Adversarial Noise Bypasses ML Classifiers#1154759
- Machine Learning Security Against Data Poisoning: Are We There Yet?#453810
- Empirical Evaluation of Data Poisoning Attacks in Supervised Learning#673423
- FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning#324901
- Correction to: Adversarial Machine Learning Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence#10807
Record · ID 998525
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive