Detection and Defense of Unlearnable Examples (AAAI-24)

This is the official repository for AAAI-24 paper "Detection and Defense of Unlearnable Examples".

Install requirements

pip install -r requirements.txt

Evaluate the poison power

python main.py --evaluation --poison-method err-min-s --dataset CIFAR-10

Detection by simple networks

python main.py --detection --detection-method simple-2NN --poison-method err-min-s --dataset CIFAR-10

Detection by bias-shifting noises

python main.py --detection --detection-method bias-0.5 --poison-method err-min-s --dataset CIFAR-10

Defense by stronger data augmentations with adversarial noises

python main.py --strong-aug --strength 1.0 --poison-method err-min-s --dataset CIFAR-10

Evaluation of criteria under adversarial training

python main.py --adv-training --poison-method err-min-s --dataset CIFAR-10

Acknowledgment

Unlearnable Examples: https://github.com/HanxunH/Unlearnable-Examples

Robust Unlearnable Examples: https://github.com/fshp971/robust-unlearnable-examples

NTGA Poisons: https://github.com/lionelmessi6410/ntga

AutoRegressive Poisons: https://github.com/psandovalsegura/autoregressive-poisoning

Hypocritical Poisons: https://github.com/TLMichael/Hypocritical-Perturbation

EntF Poisons: https://github.com/WenRuiUSTC/EntF