targeted-black-box-attack

Code of Targeted Black Box Adversarial Attack Method for Image Classification Models, published on IJCNN 2019.

1. Files

• MNIST.py: train a model on MNIST dataset.

• AdvMNIST.py: train an adversarial attack model on MNIST dataset.

• CIFAR10.py: train a model on CIFAR10 dataset.

• AdvCIFAR10.py: train an adversarial attack model on CIFAR10 dataset.

• CIFAR100.py: train a model on CIFAR100 dataset.

• AdvCIFAR100.py: train an adversarial attack model on CIFAR100 dataset.

• CIFAR10.py: train a model on CIFAR10 dataset.

• AdvCIFAR10.py: train a adversarial attack model on CIFAR10 dataset.

• AdvMNIST_sk.py: train an adversarial model on MNIST dataset to attack Naive Bayes, Decision Tree, and Random Forest et al.

• FashionMNIST.py: train a model on MNIST dataset.

• AdvFashionMNIST.py: train an adversarial attack model on MNIST dataset.

2. Datasets

• The datasets can be downloaded from https://1drv.ms/u/s!AgmtMXmpucYFgRbUnDWq7sM460St?e=QpwRpE

• You can dump MNIST, FashionMNIST, CIFAR10, and CIFAR100 datasets into h5 files rather than download the files. (MNIST.h5, FashionMNIST.h5, CIFAR10.h5, CIFAR100.h5)

3. Test environment:

• CPU: Intel E5-2430 X 2, GPU: NVIDIA GTX1070Ti, tensorflow 1.8-1.14

4. Notes:

• You can change the net body to try different networks.

• SimpleV1C, SimpleV3, and SimpleV7 in the code correspond to SmallNet, SimpleNet, and ConcatNet in the paper respectively.