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 Random Forest. Clean training is compared with poisoning rates of 5%, 10%, and 20% using clean-test accuracy, macro-precision, macro-recall, macro-F1, and, for backdoors, attack success rate. Label flipping caused clear degradation, largest for Logistic Regression and Linear SVM, while Random Forest stayed comparatively stable. Backdoor poisoning reached attack success rates from 0.9667 to 1.0000...
| Authors | Toshif Khan, Muhammad Abusaqer |
|---|---|
| Primary category | cs.CR |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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