code release for USENIX'22 paper `On the Security Risks of AutoML`

Overview

This project is a minimized runnable project cut from trojanzoo, which contains more datasets, models, attacks and defenses. This repo will not be maintained.

This is a minimum code implementation of our USENIX'22 paper On the Security Risks of AutoML.

Abstract

The artifact discovers the vulnerability gap between manual models and automl models against various kinds of attacks (adversarial, poison, backdoor, extraction and membership) in image classification domain. It implements all datasets, models, and attacks used in our paper.
We expect the artifact could support the paper's claim that automl models are more vulnerable than manual models against various kinds of attacks, which could be explained by their small gradient variance.

Checklist

  • Binary: on pypi with any platform.
  • Model: ResNet and other model pretrained weights are available with --official flag to download them automatically at first running.
  • Data set: CIFAR10, CIFAR100 and ImageNet32.
    Use --download flag to download them automatically at first running.
    ImageNet32 requires manual set-up at their website due to legality.
  • Run-time environment:
    At any platform (Windows and Ubuntu tested).
    Pytorch and torchvision required. (CUDA recommended)
    adversarial-robustness-toolbox required for extraction attack and membership attack.
  • Hardware: GPU with CUDA support is recommended.
  • Execution: Model training and backdoor attack would be time-consuming. It would cost more than half day on a Nvidia Quodro RTX6000.
  • Metrics: Model accuracy, attack success rate, clean accuracy drop, cross entropy, f1 score, and auc.
  • Output: console output and saved model files (.pth).
  • Experiments: OS scripts.
  • How much disk space is required (approximately):
    less than 5GB.
  • How much time is needed to prepare workflow (approximately): within 1 hour.
  • How much time is needed to complete experiments (approximately): 3-4 days.
  • Publicly available: on GitHub.
  • Code licenses: GPL-3.
  • Archived: GitHub commit #XXXXXXX (todo).

Description

How to access

Hardware Dependencies

Recommend to use GPU with CUDA and CUDNN.
Less than 5GB disk space is needed.

Software Dependencies

You need to install python==3.9, pytorch==1.9.x, torchvision==0.10.x manually.

ART (IBM) required for extraction attack and membership attack.
pip install adversarial-robustness-toolbox

Data set

CIFAR10, CIFAR100 and ImageNet32.
Use --download flag to download them automatically at first running.
ImageNet32 requires manual set-up at their website due to legality.

Models

ResNet and other model pretrained weights are available with --official flag to download them automatically at first running.

Installation

(optional) Config Path

You can set the config files to customize data storage location and many other default settings. View /configs_example as an example config setting.
We support 3 configs (priority ascend):

  • package:
    (DO NOT MODIFY)
    autovul/base/configs/*.yml
    autovul/vision/configs/*.yml
  • user:
    ~/.autovul/configs/base/*.yml
    ~/.autovul/configs/vision/*.yml
  • workspace:
    ./configs/base/*.yml
    ./configs/vision/*.yml

Experiment Workflow

Bash Files

Check the bash files under /bash to reproduce our paper results.

Download Datasets

If you run it for the first time, please run bash ./bash/train.sh "--download" to download the dataset.

Train Models

You need to first run /bash/train.sh to get pretrained models.

Run Attacks

/bash/adv_attack.sh
/bash/poison.sh
/bash/backdoor.sh
/bash/extraction.sh
/bash/membership.sh

Run Other Exps

/bash/grad_var.sh
/bash/mitigation_backdoor.sh
/bash/mitigation_extraction.sh

For mitigation experiments, the architecture names in our paper map to:

  • darts-i : diy_deep
  • darts-ii : diy_no_skip
  • darts-iii: diy_deep_noskip

These are the 3 options for --model_arch {arch} (with --model darts)

Evaluation and Expected Result

Our paper claims that automl models are more vulnerable than manual models against various kinds of attacks, which could be explained by low gradient variance. Therefore, for each attack, we expect automl models to have:

Train

Most models around 96%-97% accuracy on CIFAR10.

Attack

For automl models on CIFAR10,

  • adversarial
    higher success rate (around 10%).
  • poison
    lower accuracy drop (around 5%).
  • backdoor
    higher success rate (around 2%) lower accuracy drop (around 1%).
  • extraction
    lower inference cross entropy (around 0.3).
  • membership
    higher auc (around 0.04).

Others

  • gradient variance
    automl with lower gradient variance (around 2.2).
  • mitigation architecture
    deep architectures (darts-i, darts-iii) have larger cross entropy for extraction attack (around 0.5), and higher accuracy drop for poisoning attack (around 7%).

Experiment Customization

Use -h or --help flag for example python files to check available arguments.

Comments
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  • Reproduction of Attack Effectiveness of Membership Inference Attacks

    Reproduction of Attack Effectiveness of Membership Inference Attacks

    Thanks for sharing the source code of your excellent work!

    I tried to reproduce the experimental results of label-only membership inference attacks against various architectures in your paper. Here I followed the parameter settings in your paper (see Appendix B for more details) and the parameter settings in membership.py were modified as follows:

    max_iter = 50
    max_eval = 2500
    sample_size = 1000
    init_size = 100
    init_eval = 100
    

    And also, I used your pretrained models from Google Drive to conduct the experiments on the CIFAR10 dataset. The experimental results on the CIFAR10 dataset are shown below.

    |Architecture|AUC| |:-:|:-:| |BiT|0.5392| |DenseNet|0.5141| |DLA|0.5060| |ResNet|0.5049| |ResNext|0.5043| |VGG|0.6070| |WideResnet|0.5352| |AmoebaNet|0.5029| |DARTS|0.5220| |DrNAS|0.5192| |ENAS|0.5069| |NASNet|0.5285| |PC-DARTS|0.5087| |PDARTS|0.5271| |SGAS|0.5038| |SNAS|0.5081| |Random|0.5023|

    However, the experimental results show a phenomenon contrary to what you present in your paper, i.e., the manual architectures seem to be more vulnerable to membership inference attacks than the NAS architectures.

    Is there anything wrong with my parameter settings (I only modified the default parameter settings of membership.py in my experiments)? Or, do I need anything more to reproduce the experimental results of your paper?

    Thanks in advance!

    opened by MiracleHH 0
Owner
Ren Pang
Ren Pang, PhD at Penn State IST. Working on deep learning security about adversarial and backdoor attacks/defenses.
Ren Pang
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