eduardoandrade/lat_adversarial_robustness

LAT adversarial_robustness

A fine tuning technique over the adversarially trained models to increase further robustness

Running the code

Dataset: CIFAR10

Fetching LAT robust model

The model can be downloaded from this link - https://drive.google.com/open?id=1um2zoVYYw5YZuuV8_IeoUy-qRWSmCVUb> .

Evaluating the LAT robust model

python eval.py

The trained model achieves test accuracy of 87.8% and adversarial robustness of 53.82% against PGD attack(epsilon = 8.0/255.0)

Fetching Adversarial Trained Model

python fetch_model.py

Training via LAT

python feature_adv_training_layer11.py

Latent Attack

python latent_adversarial_attack.py

Example original and adversarial images computed via Latent Attack on CIFAR10

Example original and adversarial images computed via Latent Attack on Restricted Imagenet(https://arxiv.org/pdf/1805.12152.pdf.