AdvBox

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

https://github.com/advboxes/AdvBox

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
    2 of 19 committers (10.5%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (9.2%) to scientific vocabulary

Keywords

adversarial-attacks adversarial-example adversarial-examples deep-learning deepfool fgsm graphpipe machine-learning onnx paddlepaddle security
Last synced: 6 months ago · JSON representation

Repository

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

Basic Info
  • Host: GitHub
  • Owner: advboxes
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 99.3 MB
Statistics
  • Stars: 1,393
  • Watchers: 54
  • Forks: 265
  • Open Issues: 15
  • Releases: 0
Topics
adversarial-attacks adversarial-example adversarial-examples deep-learning deepfool fgsm graphpipe machine-learning onnx paddlepaddle security
Created over 7 years ago · Last pushed about 3 years ago
Metadata Files
Readme License

README.md

Advbox Family

logo

Advbox Family is a series of AI model security tools set of Baidu Open Source,including the generation, detection and protection of adversarial examples, as well as attack and defense cases for different AI applications.

Advbox Family support Python 3.*.

Our Work

AdvSDK

A Lightweight Adv SDK For PaddlePaddle to generate adversarial examples.

Homepage of AdvSDK

AdversarialBox

Adversarialbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models.Advbox give a command line tool to generate adversarial examples with Zero-Coding. It is inspired and based on FoolBox v1.

Homepage of AdversarialBox

AdvDetect

AdvDetect is a toolbox to detect adversarial examples from massive data.

Homepage of AdvDetect

AdvPoison

Data poisoning

AI applications

Face Recognition Attack

Homepage of Face Recognition Attack

Stealth T-shirt

On defcon, we demonstrated T-shirts that can disappear under smart cameras. Under this sub-project, we open-source the programs and deployment methods of smart cameras for demonstration.

Homepage of Stealth T-shirt

pic1

Fake Face Detect

The restful API is used to detect whether the face in the picture/video is a false face.

Homepage of Fake Face Detect

pic2

Paper and ppt of Advbox Family

How to cite

If you use AdvBox in an academic publication, please cite as:

@misc{goodman2020advbox,
    title={Advbox: a toolbox to generate adversarial examples that fool neural networks},
    author={Dou Goodman and Hao Xin and Wang Yang and Wu Yuesheng and Xiong Junfeng and Zhang Huan},
    year={2020},
    eprint={2001.05574},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Cloud-based Image Classification Service is Not Robust to Affine Transformation: A Forgotten Battlefield

@inproceedings{goodman2019cloud,
  title={Cloud-based Image Classification Service is Not Robust to Affine Transformation: A Forgotten Battlefield},
  author={Goodman, Dou and Hao, Xin and Wang, Yang and Tang, Jiawei and Jia, Yunhan and Wei, Tao and others},
  booktitle={Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop},
  pages={43--43},
  year={2019},
  organization={ACM}
}

Who use/cite AdvBox

  • Wu, Winston and Arendt, Dustin and Volkova, Svitlana; Evaluating Neural Model Robustness for Machine Comprehension; Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021, pp. 2470-2481
  • Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu, Xingqun Jiang; A Tour of Convolutional Networks Guided by Linear Interpreters; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 4753-4762
  • Ling, Xiang and Ji, Shouling and Zou, Jiaxu and Wang, Jiannan and Wu, Chunming and Li, Bo and Wang, Ting; Deepsec: A uniform platform for security analysis of deep learning model ; IEEE S&P, 2019
  • Deng, Ting and Zeng, Zhigang; Generate adversarial examples by spatially perturbing on the meaningful area; Pattern Recognition Letters[J], 2019, pp. 632-638

Issues report

https://github.com/baidu/AdvBox/issues

License

AdvBox support Apache License 2.0

Owner

  • Name: AdvBox
  • Login: advboxes
  • Kind: organization

AI Security and Robustness Benchmarks

GitHub Events

Total
  • Watch event: 19
  • Fork event: 1
Last Year
  • Watch event: 19
  • Fork event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 351
  • Total Committers: 19
  • Avg Commits per committer: 18.474
  • Development Distribution Score (DDS): 0.145
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
maidou 2****3@q****m 300
Tristan-Hao 4****o 16
lea4n 1****0@1****m 7
wangyang62 w****2@b****m 4
wayangGit w****4@g****m 3
Jay Xiong x****e@1****m 3
wuysh w****1@1****m 3
ezhong e****g@b****m 2
root w****g@b****m 2
saddenlar s****r@1****m 2
Haofan Wang f****e@o****m 1
0SillyMonkey0 w****8@g****m 1
Xiaozhe Yao x****i@g****m 1
cody w****g@g****m 1
dailydreamer l****2@o****m 1
tanzhongyi t****i@b****m 1
root r****t@y****m 1
Fatih ERDOGAN g****7@s****r 1
txyugood t****d@1****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 37
  • Total pull requests: 63
  • Average time to close issues: 29 days
  • Average time to close pull requests: 3 months
  • Total issue authors: 33
  • Total pull request authors: 13
  • Average comments per issue: 1.54
  • Average comments per pull request: 0.63
  • Merged pull requests: 25
  • Bot issues: 0
  • Bot pull requests: 36
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • kqvd (3)
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Pull Request Authors
  • dependabot[bot] (36)
  • lea4n (11)
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  • gabrielhao (1)
  • 0SillyMonkey0 (1)
  • xzyaoi (1)
  • txyugood (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (36)

Dependencies

.github/workflows/codeql-analysis.yml actions
  • actions/checkout v3 composite
  • github/codeql-action/analyze v2 composite
  • github/codeql-action/autobuild v2 composite
  • github/codeql-action/init v2 composite
requirements-gpu.txt pypi
  • Keras ==2.2.2
  • Keras-Applications ==1.0.4
  • Keras-Preprocessing ==1.0.2
  • Markdown ==2.6.11
  • Pillow ==5.2.0
  • PyYAML ==3.13
  • Werkzeug ==0.14.1
  • absl-py ==0.4.1
  • astor ==0.7.1
  • backports.functools-lru-cache ==1.5
  • backports.weakref ==1.0.post1
  • certifi ==2018.8.24
  • cycler ==0.10.0
  • enum34 ==1.1.6
  • funcsigs ==1.0.2
  • futures ==3.2.0
  • gast ==0.2.0
  • grpcio ==1.15.0
  • h5py ==2.8.0
  • kiwisolver ==1.0.1
  • matplotlib ==2.2.3
  • mock ==2.0.0
  • numpy ==1.14.5
  • pbr ==4.2.0
  • protobuf ==3.6.1
  • pyparsing ==2.2.0
  • python-dateutil ==2.7.3
  • pytz ==2018.5
  • scipy ==1.1.0
  • six ==1.11.0
  • subprocess32 ==3.5.2
  • tensorboard ==1.10.0
  • tensorflow-gpu ==1.10.1
  • termcolor ==1.1.0
requirements.txt pypi
  • Keras ==2.2.2
  • Keras-Applications ==1.0.4
  • Keras-Preprocessing ==1.0.2
  • Markdown ==2.6.11
  • Pillow ==5.2.0
  • PyYAML ==3.13
  • Werkzeug ==0.14.1
  • absl-py ==0.4.1
  • astor ==0.7.1
  • backports.functools-lru-cache ==1.5
  • backports.weakref ==1.0.post1
  • certifi ==2018.8.24
  • cycler ==0.10.0
  • enum34 ==1.1.6
  • funcsigs ==1.0.2
  • futures ==3.2.0
  • gast ==0.2.0
  • graphpipe ==1.0.4
  • grpcio ==1.15.0
  • h5py ==2.8.0
  • kiwisolver ==1.0.1
  • matplotlib ==2.2.3
  • mock ==2.0.0
  • numpy ==1.14.5
  • pbr ==4.2.0
  • protobuf ==3.6.1
  • pyparsing ==2.2.0
  • python-dateutil ==2.7.3
  • pytz ==2018.5
  • scipy ==1.1.0
  • six ==1.11.0
  • subprocess32 ==3.5.2
  • tensorboard ==1.10.0
  • tensorflow ==1.10.1
  • termcolor ==1.1.0
setup.py pypi
  • GitPython *
  • numpy *
  • requests *
  • scipy *
  • setuptools *