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README.md

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

Overview

This is the code repository for the KDD 2018 Applied Data Science paper: SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression. Visit our research group homepage Polo Club of Data Science at Georgia Tech for more related research!

The code included here reproduces our techniques (e.g. SLQ) presented in the paper, and also our experiment results reported, such as using various JPEG compression qualities to remove adversarial perturbation introduced by Carlini-Wagner-L2, DeepFool, I-FSGM, and FSGM.

SHIELD overview YouTube video

Research Abstract

The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This underscores the urgent need for practical defense that can be readily deployed to combat attacks in real-time. Observing that many attack strategies aim to perturb image pixels in ways that are visually imperceptible, we place JPEG compression at the core of our proposed SHIELD defense framework, utilizing its capability to effectively "compress away" such pixel manipulation. To immunize a DNN model from artifacts introduced by compression, SHIELD "vaccinates" a model by re-training it with compressed images, where different compression levels are applied to generate multiple vaccinated models that are ultimately used together in an ensemble defense. On top of that, SHIELD adds an additional layer of protection by employing randomization at test time that compresses different regions of an image using random compression levels, making it harder for an adversary to estimate the transformation performed. This novel combination of vaccination, ensembling, and randomization makes SHIELD a fortified, multi-pronged defense. We conducted extensive, large-scale experiments using the ImageNet dataset, and show that our approaches eliminate up to 94% of black-box attacks and 98% of gray-box attacks delivered by the recent, strongest techniques, such as Carlini-Wagner's L2 and DeepFool. Our approaches are fast and work without requiring knowledge about the model.

Installation and Setup

Clone Repository

To clone this repository using git, simply run the following command:

git clone https://github.com/poloclub/jpeg-defense.git

Install Dependencies

This repository uses attacks from the CleverHans library, and the models are adapted from tf-slim. We also use Sacred to keep track of the experiments. All dependencies for this repository can be found in requirements.txt. To install these dependencies, run the following command from the jpeg-defense directory:

pip install -r requirements.txt

Setup ImageNet Dataset

The code expects the ImageNet validation dataset to be available in TFRecord format in the data/validation directory. To provision the data, we have provided a script (setup/get_imagenet.py) that downloads, processes, and saves the entire ImageNet dataset in the required format. This script can be run from the setup directory in the following manner:

python get_imagenet.py --local_scratch_dir="/path/to/jpeg-defense/data"

Downloading the entire dataset from the ImageNet website using this script may be very slow. Optionally, we recommend downloading the ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar using Academic Torrents, and placing these files into the data/raw_data directory. Then, you can run the following command to skip downloading the dataset and proceed with converting the data into TFRecord format:

python get_imagenet.py \
  --local_scratch_dir="/path/to/jpeg-defense/data" \
  --provision_only=True

Download Pre-trained Model Weights

This repository currently supports the ResNet50-v2 and Inception-v4 models from tf-slim. Running the following command from the jpeg-defense directory will download the pre-trained .ckpt files for these models into the data/checkpoints folder using the provided setup/get_model_checkpoints.sh script:

bash setup/get_model_checkpoints.sh data/checkpoints

Example Usage

The main.py script in the shield package can be used to perform all the experiments using the --perform=attack|defend|evaluate flags.

  • attack - Attacks the specified model with the specified method and its parameters (see shield/opts.py).
python main.py with \                               
  perform=attack \
  model=resnet_50_v2 \
  attack=fgsm \
  attack_options="{'eps': 16}"
  • defend - Defends the specified attacked images with the specified defense and its parameters (see shield/opts.py). The defense uses the attack parameters only to determine which images are loaded for preprocessing, as these parameters are not used by the preprocessing itself.
python main.py with \                               
  perform=defend \
  model=resnet_50_v2 \
  attack=fgsm \
  attack_options="{'eps': 16}" \
  defense=jpeg \
  defense_options="{'quality': 80}"
  • evaluate - Evaluates the specified model with the specified attacked/defended version of the images.
python main.py with \                               
  perform=evaluate \
  model=resnet_50_v2 \
  attack=fgsm \
  attack_options="{'eps': 16}"

Video Demo

YouTube video demo

Paper PDF on arXiv

Paper PDF on arXiv

Citation

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression.
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau.
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2018. London, UK. Aug 19-23, 2018.

BibTeX

@article{das2018shield, 
   title={SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression}, 
   author={Das, Nilaksh and Shanbhogue, Madhuri and Chen, Shang-Tse and Hohman, Fred and Li, Siwei and Chen, Li and Kounavis, Michael E and Chau, Duen Horng}, 
   booktitle={Proceedings of the 24nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, 
   year={2018}, 
   organization={ACM} 
}

Researchers

Name Affiliation
Nilaksh Das Georgia Tech
Madhuri Shanbhogue Georgia Tech
Shang-Tse Chen Georgia Tech
Fred Hohman Georgia Tech
Siwei Li Georgia Tech
Li Chen Intel Corporation
Michael E. Kounavis Intel Corporation
Polo Chau Georgia Tech
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