Toolbox for adversarial machine learning.
Project description
Adversarial Robustness Toolbox (ART) v1.9
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).
Adversarial Threats
ART for Red and Blue Teams (selection)
Learn more
| Get Started | Documentation | Contributing |
|---|---|---|
| - Installation - Examples - Notebooks |
- Attacks - Defences - Estimators - Metrics - Technical Documentation |
- Slack, Invitation - Contributing - Roadmap - Citing |
The library is under continuous development. Feedback, bug reports and contributions are very welcome!
Acknowledgment
This material is partially based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0013. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file adversarial-robustness-toolbox-1.9.1.tar.gz.
File metadata
- Download URL: adversarial-robustness-toolbox-1.9.1.tar.gz
- Upload date:
- Size: 2.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.25.0 setuptools/58.2.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
975fa3a5ea0026da2203800d9ce95d114568edbc4ee8c085bf0b571ac1037bcc
|
|
| MD5 |
364c9c7b946c656f5c3736eb5de953a2
|
|
| BLAKE2b-256 |
748911f26d241b63c2f063a147c9c5ea25fad801e3534e8512201ee3484492ec
|
File details
Details for the file adversarial_robustness_toolbox-1.9.1-py3-none-any.whl.
File metadata
- Download URL: adversarial_robustness_toolbox-1.9.1-py3-none-any.whl
- Upload date:
- Size: 1.2 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.25.0 setuptools/58.2.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3c88c6846582a2f5a8e1d5abcce9d6806da922c007db44265fa960b280354821
|
|
| MD5 |
f6fa3e6618599dc01a664a5e914bc6f0
|
|
| BLAKE2b-256 |
70936ec89ea92499476271adf87aeedc2853b455e059f3352abb77ed911347f0
|
