khast3x/adversarial_image_defenses

Countering Adversarial Image using Input Transformations.

Countering Adversarial Images Using Input Transformations

Overview

This package implements the experiments described in the paper Countering Adversarial Images Using Input Transformations. It contains implementations for adversarial attacks, defenses based image transformations, training, and testing convolutional networks under adversarial attacks using our defenses. We also provide pre-trained models.

If you use this code, please cite our paper:

• Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering Adversarial Images using Input Transformations. arXiv 1711.00117, 2017. [PDF]

Adversarial Defenses

The code implements the following four defenses against adversarial images, all of which are based on image transformations:

• Image quilting
• Total variation minimization
• JPEG compression
• Pixel quantization

Please refer to the paper for details on these defenses. A detailed description of the original image quilting algorithm can be found here; a detailed description of our solver for total variation minimization can be found here.

Adversarial Attacks

The code implements the following four approaches to generating adversarial images:

• Fast gradient sign method (FGSM)
• Iterative FGSM
• DeepFool
• Carlini-Wagner attack

Installation

To use this code, first install Python, PyTorch, and Faiss (to perform image quilting). We tested the code using Python 2.7, PyTorch v0.2.0, and scikit-image 0.11; your mileage may vary when using other versions.

Pytorch can be installed using the instructions here. Faiss is required to run the image quilting algorithm; it is not automatically included because faiss does not have a pip support and because it requires configuring BLAS and LAPACK flags, as described here. Please install faiss using the instructions given here.

The code uses several other external dependencies (for training Inception models, performing Bregman iteration, etc.). These dependencies are automatically downloaded and installed when you install this package via pip:

# Install from source  
cd adversarial_image_defenses
pip install .

Usage

To import the package in Python:

import adversarial

The functionality implemented in this package is demonstrated in this example. Run the example via:

python adversarial/examples/demo.py

API

The full functionality of the package is exposed via several runnable Python scripts. All these scripts require the user to specify the path to the Imagenet dataset, the path to pre-trained models, and the path to quilted images (once they are computed) in lib/path_config.json. Alternatively, the paths can be passed as input arguments into the scripts.

Generate quilting patches

index_patches.py creates a faiss index of images patches. This index can be used to perform quilting of images.

Code example:

import adversarial
from index_patches import create_faiss_patches, parse_args

args = parse_args()
# Update args if needed
args.patch_size = 5
create_faiss_patches(args)

Alternatively, run python index_patches.py. The following arguments are supported:

• --patch_size Patch size (square) that will be used in quilting (default: 5).
• --num_patches Number of patches to generate (default: 1000000).
• --pca_dims PCA dimension for faiss (default: 64).
• --patches_file File in which patches are saved.
• --index_file File in which faiss index of patches is saved.

Image transformations

gen_transformed_images.py has applies an image transformation on (adversarial or non-adversarial) ImageNet images, and saves them to disk. Image transformations such as image quilting are too computationally intensive to be performed on-the-fly during network training, which is why we precompute the transformed images.

Code example:

import adversarial
from gen_transformed_images import generate_transformed_images
from lib import opts
# load default args for transformation functions
args = opts.parse_args(opts.OptType.TRANSFORMATION)
args.operation = "transformation_on_raw"
args.defenses = ["tvm"]
args.partition_size = 1  # Number of samples to generate

generate_transformed_images(args)

Alternatively, run python gen_transformed_images.py. In addition to the common arguments and adversarial arguments, the following arguments are supported:

• --operation Operation to run. Supported operations are:
  transformation_on_raw: Apply transformations on raw images. transformation_on_adv: Apply transformations on adversarial images. cat_data: Concatenate output from distributed transformation_on_adv.
• --data_type Data type (train or raw) for transformation_on_raw (default: train).
• --out_dir Directory path for output of cat_data.
• --partition_dir Directory path to output transformed data.
• --data_batches Number of data batches to generate. Used for random crops for ensembling.
• --partition Distributed data partition (default: 0).
• --partition_size The size of each data partition.
  For transformation_on_raw, partition_size represents number of classes for each process.
  For transformation_on_adv, partition_size represents number of images for each process.
• --n_threads Number of threads for transformation_on_raw.

Generate TAR data index

Many file systems perform poorly when dealing with millions of small files (such as images). Therefore, we generally TAR our image datasets (obtained by running generate_transformed_images). Next, we use gen_tar_index.py to generate a file index for the TAR file. The file index facilitates fast, random-access reading of the TAR file; it is much faster and requires less memory than untarring the data or using tarfile package.

Code example:

import adversarial
from gen_tar_index import generate_tar_index, parse_args

args = parse_args()
generate_tar_index(args)

Alternatively, run python gen_tar_index.py. The following arguments are supported:

• --tar_path Path for TAR file or directory.
• --index_root Directory in which to store TAR index file.
• --path_prefix Prefix to identify TAR member names to be indexed.

Adversarial Attacks

gen_adversarial_images.py implements the generation of adversarial images for the ImageNet dataset.

Code example:

import adversarial
from gen_adversarial_images import generate_adversarial_images
from lib import opts
# load default args for adversary functions
args = opts.parse_args(opts.OptType.ADVERSARIAL)
args.model = "resnet50"
args.adversary_to_generate = "fgs"
args.partition_size = 1  # Number of samples to generate
args.data_type = "val"  # input dataset type
args.normalize = True  # apply normalization on input data
args.attack_type = "blackbox"  # For <whitebox> attack, use transformed models
args.pretrained = True  # Use pretrained model from model-zoo

generate_adversarial_images(args)

Alternatively, run python gen_adversarial_images.py. For a list of the supported arguments, see common arguments and adversarial arguments.

Training

train_model.py implements the training of convolutional networks on (transformed or non-transformed) ImageNet images.

Code example:

import adversarial
from train_model import train_model
from lib import opts
# load default args
args = opts.parse_args(opts.OptType.TRAIN)
args.defenses = None  # defense=<(raw, tvm, quilting, jpeg, quantization)>
args.model = "resnet50"
args.normalize = True  # apply normalization on input data

train_model(args)

Alternatively, run python train_model.py. In addition to the common arguments, the following arguments are supported:

• --resume Resume training from checkpoint (if available).
• --lr Initial learning rate defined in [constants.py] (lr=0.045 for Inception-v4, 0.1 for other models).
• --lr_decay Exponential learning rate decay defined in [constants.py] (0.94 for inception_v4, 0.1 for other models).
• --lr_decay_stepsize Decay learning rate after every stepsize epochs defined in [constants.py] (0.94 for inception_v4, 0.1 for other models).
• --momentum Momentum (default: 0.9).
• --weight_decay Amount of weight decay (default: 1e-4).
• --start_epoch Index of first epoch (default: 0).
• --end_epoch Index of last epoch (default: 90).
• --preprocessed_epoch_data Augmented and transformed data for each epoch is pre-generated (default: False).

Testing

classify_images.py implements the testing of a training convolutional network on an dataset of (adversarial or non-adversarial / transformed or non-transformed) ImageNet images.

Code exammple:

import adversarial
from classify_images import classify_images
from lib import opts
# load default args
args = opts.parse_args(opts.OptType.CLASSIFY)

classify_images(args)

Alternatively, run python classify_images.py. In addition to the common arguments, the following arguments are supported:

• --ensemble Ensembling type, None, avg, max (default: None).
• --ncrops List of number of crops for each defense to use for ensembling (default: None).
• --crop_frac List of crop fraction for each defense to use for ensembling (default: None).
• --crop_type List of crop type(center, random, sliding(hardset for 9 crops)) for each defense to use for ensembling (default: None).

Pre-trained models

We provide pre-trained models that were trained on ImageNet images that were processed using total variation minimization (TVM) or image quilting can be downloaded from the following links (set the models_root argument to the path that contains these model model files):

• ResNet-50_model trained on quilted images
• ResNet-50_model trained on TVM images
• ResNet-101_model trained on quilted images
• ResNet-101_model trained on TVM images
• DenseNet-169_model trained on quilted images
• DenseNet-169_model trained on TVM images
• Inception-v4_model trained on quilted images
• Inception-v4_model trained on TVM images

Common arguments

The following arguments are used by multiple scripts, including generate_transformed_images, train_model, and classify_images:

Paths

• --data_root Main data directory to save and read data.
• --models_root Directory path to store/load models.
• --tar_dir Directory path for transformed images(train/val) stored in TAR files.
• --tar_index_dir Directory path for index files for transformed images in TAR files.
• --quilting_index_root Directory path for quilting index files.
• --quilting_patch_root Directory path for quilting patch files.

Train/Classifier params

• --model Model to use (default: resnet50).
• --device Device to use: cpu or gpu (default: gpu).
• --normalize Normalize image data.
• --batchsize Batch size for training and testing (default: 256).
• --preprocessed_data Transformations/Defenses are already applied on saved images (default: False).
• --defenses List of defenses to apply: raw (no defense), tvm, quilting, jpeg, quantization (default: None).
• --pretrained Use pretrained model from PyTorch model zoo (default: False).

Tranformation params

• --tvm_weight Regularization weight for total variation minimization (TVM).
• --pixel_drop_rate Pixel drop rate to use in TVM.
• --tvm_method Reconstruction method to use in TVM (default: bregman).
• --quilting_patch_size Patch size to use in image quilting.
• --quilting_neighbors Number of nearest patches to sample from in image quilting (default: 1).
• --quantize_depth Bit depth for quantization defense (default: 8).

Adversarial arguments

The following arguments are used whem generating adversarial images with gen_transformed_images.py:

• --n_samples Maximum number of samples to test on.
• --attack_type Attack type: None (no attack), blackbox, whitebox (default: None).
• --adversary Adversary to use: fgs, ifgs, cwl2, deepfool (default: None).
• --adversary_model Model to use for generating adversarial images (default: resnet50).
• --learning_rate Learning rate for iterative adversarial attacks (default: read from constants).
• --adv_strength Adversarial strength for non-iterative adversarial attacks (default: read from constants).
• --adversarial_root Path containing adversarial images.