This repository contains the PyTorch implementation with adversarial-robust training objective introduced in the following paper:
Improving Adversarial Robustness via Guided Complement Entropy.
Hao-Yun Chen*, Jhao-Hong Liang*, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan.
https://arxiv.org/abs/1903.09799
In this paper, we propose a new training paradigm called Guided Complement Entropy (GCE) that is capable of achieving "adversarial defense for free," which involves no additional procedures in the process of improving adversarial robustness. In addition to maximizing model probabilities on the ground-truth class like cross-entropy, we neutralize its probabilities on the incorrect classes along with a "guided" term to balance between these two terms. We show in the experiments that our method achieves better model robustness with even better performance compared to the commonly used cross-entropy training objective.
We demonstrate model accuracies trained with different training objective: Baseline (cross-entropy) and GCE (Guided Complement Entropy) under various of SOTA white-box attacks.
MNIST is used to be the benchmark dataset and the perturbations are all set to be 0.2.
| Attack | Baseline | GCE |
|---|---|---|
| FGSM (Goodfellow et al. 2015) | 38.88% | 62.74% |
| MIM (Dong et al. 2018) | 2.29% | 39.81% |
| PGD (Madry et al. 2018) | 1.58% | 9.55% |
For training a robust model with Guided Complement Entropy. (alpha as the strength in our proposed guided factor)
python main.py --GCE --alpha 0.333
For testing the robustness to PGD adversarial attacks on the previous training model.
python LinfPGDAttack.py --GCE --model <model path>
- Python 3.6
- Pytorch 1.0 +
The implementations for PGD adversarial attacks are adapted from Advertorch.