The PyTorch implementation of our paper:
Chenchen Zhao, and Hao Li. Condition-Invariant Physical Adversarial Attacks via Pixel-Wise Adversarial Learning. International Conference on Neural Information Processing 2021
[paper] [learn more]
We propose a physical adversarial attack method that maintains high robustness against multiple simulated real-world disturbances (e.g. imbalanced illumination, long distances, noise in capturing, etc). The model reaches state-of-the-art physical attack performance, and is the first one to propose the concept of pixel-wise adversarial learning in adversarial attacks
1.5 years later...
- Rewrite the code
Run conda env create -f environment.yaml && conda activate palpha to create and activate a conda virtual environment named palpha
Run python main.py attack to conduct P-ALPhA on a pretrained classifier
Run python main.py test to validate the performance of the model against different simulated real-world disturbances
Modify args.py for customized experimental settings
Modify model.py for customized classifier
This project does not include classifier-training scripts. Pre-train a classifier, and replace
model.pywith customized classifier definition. The classifier should only have logits as output
| ResNet18 | Success rate % | Illumination % | Distances % | Errors % |
|---|---|---|---|---|
| PGD & P-ALPhA | 100.0 | 98.0 | 100.0 | 91.8 |
| PGD | 100.0 | 6.07 | 4.49 | 3.34 |
| EoT 1 | 16.0 | 40.5 | 87.3 | 77.9 |
| C&W & P-ALPhA | 100.0 | 99.0 | 79.6 | 59.2 |
| C&W | 100.0 | 92.9 | 75.5 | 30.6 |
| VGG16 | Success rate % | Illumination % | Distances % | Errors % |
|---|---|---|---|---|
| PGD & P-ALPhA | 99.0 | 95.9 | 100.0 | 74.5 |
| PGD | 100.0 | 8.08 | 6.06 | 12.1 |
| EoT 1 | 50.5 | 12.0 | 54.0 | 12.0 |
| C&W & P-ALPhA | 100.0 | 39.4 | 53.5 | 11.1 |
| C&W | 100.0 | 33.3 | 53.5 | 13.1 |
@inproceedings{zhao2021condition,
title={Condition-Invariant Physical Adversarial Attacks via Pixel-Wise Adversarial Learning},
author={Zhao, Chenchen and Li, Hao},
booktitle={International Conference on Neural Information Processing},
pages={369--380},
year={2021},
organization={Springer}
}