Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks (NeurIPS 2022)

Official code repository for Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks.

Authors: Tian Yu Liu, Yang Yu, and Baharan Mirzasoleiman

Dataset preparation

For preparation of poisoned datasets, please follow: https://github.com/YuYang0901/EPIC

Example Usage

For training with friendly noise and bernoulli noise.

python main.py --noise_type friendly bernoulli --friendly_begin_epoch 5 --poisons_path $POISON_DATASET --out result 

Acknowledgements

• https://github.com/JonasGeiping/poisoning-gradient-matching
• https://github.com/aks2203/poisoning-benchmark