# Python dependencies:
enum34
numpy
scipy
tensorflow-gpu
tqdm
For viewing examples, additionally jupyter and matplotlib
# Running the attacks
## Feature squeezing
### Setup, CIFAR-10
# Set up the pre-trained weights
ln -s "$PWD/resnet_model" /tmp
# Extract a batch of images to a numpy array
cd models
python resnet/save-unquantized.py \
--eval_data_path=cifar10/test_batch.bin \
--log_root=/tmp/resnet_model \
--eval_dir=/tmp/resnet_model/test \
--mode=eval \
--dataset='cifar10' \
--num_gpus=1
### Color depth reduction, CIFAR-10
cd models
python resnet/baseline2-adv.py
python resnet/precision-adv-test.py
Configure number of bits by changing the parameter to `reduce_precision_tf`, for example, the following reduces to 3 bits (8 levels):
x_star_quantized = squeeze.reduce_precision_tf(x_star, 8)
### Spatial smoothing, CIFAR-10
cd models
python resnet/smoothing-adv.py
python resnet/smoothing-adv-test.py
Configure the median filter size by changing the parameter to `median_filter`, for example, the following smooths with a 2x2 filter:
x_star_med = median.median_filter(x_star, 2, 2)
### Detection, CIFAR-10
cd models
python resnet/combined-adv.py
python resnet/combined-adv-test.py
### Color depth reduction, MNIST
cd mnist_adv
# 1-bit, 100 images, 5000 iterations, yes (1) use pretrained model
python quantization_no_gs.py 1 100 5000 1
### Spatial smoothing, MNIST
cd mnist_adv
# 3x3, 100 images, 5000 iterations, yes (1) use pretrained model
python median.py 3 3 100 5000 1
### Detection, MNIST
cd mnist_adv
# 1-bit, 2x2, 100 images, 150 iterations, yes (1) use pretrained model, 100 random initializations
python detection-onemodel.py 1 2 2 100 150 1 100
## Ensemble of specialists
### MNIST
cd ensemble-specialists
python save-originals-mnist.py
python adv-mmist.py
python adv-mnist-test.py