LitterQ / Perturbation-Diversity

PERTURBATION DIVERSITY CERTIFICATES ROBUST GENERALIZATION

Introduction

This is the implementation of the ["PERTURBATION DIVERSITY CERTIFICATES ROBUST GENERALIZATION"].

The codes are implemented based on the released codes from "Feature-Scattering Adversarial Training" [Code] [Paper]

Tested under Python 3.7.9 and PyTorch 1.8.0.

Train

Enter the folder named by the dataset you want to train. Specify the path for saving the trained models in fs_train.sh, and then run

sh ./fs_train_cfiar10.sh  # for CIFAR-10 dataset
sh ./fs_train_svhn.sh     # for SVHN dataset
sh ./fs_train_cfiar100.sh # for CIFAR-100 dataset

Evaluate

Specify the path to the trained models to be evaluated in fs_eval.sh and then run, using CIFAR-10 as a example. param: --init_model_pass: The load number of checkpoint, Possible values: `latest` for the last checkpoint, `199` for checkpoint-199 param: --attack_method_list: The attack list for evaluation, Possible values: `natural` for natural data, `fgsm`, `pgd`, `cw`

sh ./fs_eval_cifar10.sh

Reference Model

A reference AT+PD model trained on CIFAR-10, CIFAR-100, SVHN.

A reference FS+PD model trained on CIFAR-10, CIFAR-100, SVHN.

Reference

Haichao Zhang and JianyuWang. Defense against adversarial attacks using feature scattering-based adversarial training. In Advances in Neural Information Processing Systems, pp. 1829–1839, 2019.