{ "info": { "author": "KakaoBrain", "author_email": "kwk236@nyu.edu", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.4" ], "description": "\n.. raw:: html\n\n
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\n\nIntro\n-----\n\n**Detox** is an open source software library for machine learning\nsecurity. It contains tools for adversarial example generation and\nprovides a framework for building new types of attack methods.\n\nCurrently in the dev stage.\n\nAttacks\n-------\n\nAvailable attack algorithms implemented in Detox:\n\n- Fast Gradient Methods (FGM/FGSM)\n ```Tutorial`` `__\n- Basic Iterative\n ```Tutorial`` `__\n- Momentum Iterative\n ```Tutorial`` `__\n- DeepFool\n- Universal Adversarial Perturbation (UAP)\n- Jacobian-based Saliency Map Approach (JSMA)\n- One Pixel Attack\n- LBFGS\n- Carlini Wagner L2\n- Carlini Wagner L-inf\n- Feature Adversaries\n- Boundary Attack\n- Elastic Net\n- Natural Adversarial Examples (NAE)\n\nThe Team\n~~~~~~~~\n\nDetox is a community driven project. The project was initiated by\nmachine learning security team @ `KakaoBrain `__.\n\n.. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/KakaoBrain", "keywords": "machine learning security adversarial attackdefense deep learning pytorch", "license": "MIT License", "maintainer": "", "maintainer_email": "", "name": "dtox", "package_url": "https://pypi.org/project/dtox/", "platform": "", "project_url": "https://pypi.org/project/dtox/", "project_urls": { "Homepage": "https://github.com/KakaoBrain" }, "release_url": "https://pypi.org/project/dtox/0.1.0/", "requires_dist": [ "numpy (>=1.7)", "scipy (>=0.19.0)", "torch", "six (>=1.10.0)", "torchvision; extra == 'dev'", "flake8; extra == 'dev'", "yapf; extra == 'dev'", "isort; extra == 'dev'", "pytest; extra == 'dev'", "pytest-xdist; extra == 'dev'", "nbval; extra == 'dev'", "nbstripout; extra == 'dev'", "pypandoc; extra == 'dev'", "sphinx; extra == 'dev'", "sphinx-rtd-theme; extra == 'dev'", "jupyter (>=1.0.0); extra == 'notebooks'", "prettytable; extra == 'profile'", "pytest; extra == 'test'", "pytest-cov; extra == 'test'", "nbval; extra == 'test'", "visdom; extra == 'test'", "torchvision; extra == 'test'", "matplotlib (>=1.3); extra == 'visualization'", "visdom (>=0.1.4); extra == 'visualization'", "pillow; extra == 'visualization'" ], "requires_python": "", "summary": "TODO", "version": "0.1.0" }, "last_serial": 3926784, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "09e02fb21bde8838f3b55267d025df9a", "sha256": "a431e8c1c5cd3cd978e4b8ed6316df254b1c3838ead95d6edeb1d9c847f56393" }, "downloads": -1, "filename": "dtox-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "09e02fb21bde8838f3b55267d025df9a", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 26651, "upload_time": "2018-06-04T02:49:59", "url": "https://files.pythonhosted.org/packages/e3/2f/c4a25c97b54fef13ba61e7b3a256fbb45576f4b0055a0b660144388ea7c6/dtox-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "bedc4ee540253dfa6a52fc3368f53607", "sha256": "d397e724ea11eacceae9beb5b455a31e76161d7ba587198557974adabb40082a" }, "downloads": -1, "filename": "dtox-0.1.0.tar.gz", "has_sig": false, "md5_digest": "bedc4ee540253dfa6a52fc3368f53607", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 17881, "upload_time": "2018-06-04T02:50:00", "url": "https://files.pythonhosted.org/packages/49/7f/728694df3e0b43e033e6b0d239dc7ced976db5ff126b3c63c50a8c798276/dtox-0.1.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "09e02fb21bde8838f3b55267d025df9a", "sha256": "a431e8c1c5cd3cd978e4b8ed6316df254b1c3838ead95d6edeb1d9c847f56393" }, "downloads": -1, "filename": "dtox-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "09e02fb21bde8838f3b55267d025df9a", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 26651, "upload_time": "2018-06-04T02:49:59", "url": "https://files.pythonhosted.org/packages/e3/2f/c4a25c97b54fef13ba61e7b3a256fbb45576f4b0055a0b660144388ea7c6/dtox-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "bedc4ee540253dfa6a52fc3368f53607", "sha256": "d397e724ea11eacceae9beb5b455a31e76161d7ba587198557974adabb40082a" }, "downloads": -1, "filename": "dtox-0.1.0.tar.gz", "has_sig": false, "md5_digest": "bedc4ee540253dfa6a52fc3368f53607", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 17881, "upload_time": "2018-06-04T02:50:00", "url": "https://files.pythonhosted.org/packages/49/7f/728694df3e0b43e033e6b0d239dc7ced976db5ff126b3c63c50a8c798276/dtox-0.1.0.tar.gz" } ] }