Awesome
Explainability-Aware One Point Attack for Point Cloud Neural Networks
Pytorch implementation for Explainability-Aware One Point Attack for Point Cloud Neural Networks. Point cloud neural networks are based on this repo. Please follow the instructions to train networks before attacking them.
Environments
Python >= 3.6 Pytorch >= 1.6.0
Usage
Before running the code, move the test data file list modelnet40_test_adv.txt to ./data/modelnet40_normal_resampled/ or generate user-defined number of test files using sample_adv_test_data.py (also should be placed in the ./data/modelnet40_normal_resampled/ path):
python sample_adv_test_data.py
Create visualization path:
mkdir visu
cd visu
mkdir output
Test and visualize one instance randomly picked up from dataset with OPA and CTA respectively:
python Test_single_ins_OPA.py
python Test_single_ins_CTA.py
<img src="https://github.com/Explain3D/Exp-One-Point-Atk-PC/blob/main/pic/exp_opa.png?raw=true" width="400px">
Quantitatively evaluate the attack performance:
python Eval_OPA.py
python Eval_CTA.py