Reverse Cross Entropy Training (RCE) is a novel training method, which can learn more distinguished feature representations for detecting adversarial examples. Technical details are specified in:
Towards Robust Detection of Adversarial Examples (NeurIPS 2018)
Tianyu Pang, Chao Du, Yinpeng Dong and Jun Zhu
We provide codes for training ResNet on MNIST and CIFAR-10. Our codes are based on Tensorflow.
Prerequisite:
How to run:
An example of using RCE to train a ResNet-32 on MNIST:
python train.py --train_data_path='mnist_dataset/data_train.bin' \
--log_root=models_mnist/resnet32 \
--train_dir=models_mnist/resnet32/train \
--dataset='mnist' \
--num_gpus=1 \
--num_residual_units=5 \
--mode=train \
--Optimizer='mom' \
--total_steps=20000 \
--RCE_train=TrueAn example of test trained ResNet-32 in the normal setting (test set) on MNIST:
python test_nor.py --eval_data_path='mnist_dataset/data_test.bin' \
--log_root=models_mnist/resnet32 \
--eval_dir=models_mnist/resnet32/eval \
--dataset='mnist' \
--num_gpus=1 \
--num_residual_units=5 \
--mode=eval \
--RCE_train=TrueThe code of attacks is forked from Zhitao Gong
An example of attacking a trained Resnet-32 by FGSM on MNIST:
python test_adv.py --eval_data_path='mnist_dataset/test_batch.bin' \
--log_root=models_mnist/resnet32 \
--dataset='mnist' \
--num_gpus=1 \
--num_residual_units=5 \
--Optimizer='mom' \
--mode=attack \
--RCE_train=True \
--attack_method='fgsm' \
--eval_batch_count=5The attack_method could be random, fgsm (FGSM), bim (BIM), tgsm (ILCM), jsma (JSMA), carliniL2 (C&W), carliniL2_highcon (C&W-highcon) and carliniL2_specific (C&W-whitebox).
After running the attacking codes, there will be saved files containing the information of crafted adversarial exmaples. To further perform detection between adversarial examples and normal ones, there are three extra steps to do:
First Step: Get the train_logits
python others.py --eval_data_path='mnist_dataset/data_train.bin' \
--log_root=models_mnist/resnet32 \
--dataset='mnist' \
--num_gpus=1 \
--num_residual_units=5 \
--mode=kernel_para \
--Optimizer='mom' \
--eval_batch_count=500 \
--RCE_train=TrueSecond Step: run Matlab_scripts/select_kernel.m
Third Step: run Matlab_scripts/auc_of_roc_RCE.m