# Curie: A method for protecting SVM Classifier from Poisoning Attack

arXiv: https://arxiv.org/abs/1606.01584 · HTML: https://mlsanity.com/paper/1606.01584/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2016.

## Built on by (most cited first, 10 of 10)

- [Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning](https://mlsanity.com/paper/2205.01992/) (2022)
- [Privacy and Trust Redefined in Federated Machine Learning](https://mlsanity.com/paper/2103.15753/) (2021)
- [Adversarial Training for Defense Against Label Poisoning Attacks](https://mlsanity.com/paper/2502.17121/) (2025)
- [Defend Data Poisoning Attacks on Voice Authentication](https://mlsanity.com/paper/2209.04547/) (2022)
- [Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks](https://mlsanity.com/paper/2108.04206/) (2021)
- [Outlier-Oriented Poisoning Attack: A Grey-box Approach to Disturb Decision Boundaries by Perturbing Outliers in Multiclass Learning](https://mlsanity.com/paper/2411.00519/) (2024)
- [A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers](https://mlsanity.com/paper/2105.13530/) (2021)
- [Manipulating Trajectory Prediction with Backdoors](https://mlsanity.com/paper/2312.13863/) (2023)
- [Interactive Trimming against Evasive Online Data Manipulation Attacks: A Game-Theoretic Approach](https://mlsanity.com/paper/2403.10313/) (2024)
- [Timber! Poisoning Decision Trees](https://mlsanity.com/paper/2410.00862/) (2024)

## Abstract (arXiv)

Machine learning is used in a number of security related applications such as biometric user authentication, speaker identification etc. A type of causative integrity attack against machine learning called Poisoning attack works by injecting specially crafted data points in the training data so as to increase the false positive rate of the classifier. In the context of the biometric authentication, this means that more intruders will be classified as valid user, and in case of speaker identification system, user A will be classified user B. In this paper, we examine poisoning attack against SVM and introduce - Curie - a method to protect the SVM classifier from the poisoning attack. The basic idea of our method is to identify the poisoned data points injected by the adversary and filter them out. Our method is light weight and can be easily integrated into existing systems. Experimental results show that it works very well in filtering out the poisoned data.