{
    "info": {
        "author": "iradz",
        "author_email": "irad.zehavi@outlook.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.10",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9"
        ],
        "description": "adv-ml\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## Docs\n\nSee https://irad-zehavi.github.io/adv-ml/\n\n## Install\n\n``` sh\npip install adv_ml\n```\n\n## How to use\n\n## How to Use\n\nAs an nbdev library, `adv-ml` supports `import *` (without importing\nunwanted symbols):\n\n``` python\nfrom adv_ml.all import *\n```\n\n### Adversarial Examples\n\n``` python\nmnist = MNIST()\nclassifier = MLP(10)\nlearn = Learner(mnist.dls(), classifier, metrics=accuracy)\nlearn.fit(1)\n```\n\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>accuracy</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.154410</td>\n      <td>0.177410</td>\n      <td>0.953900</td>\n      <td>00:32</td>\n    </tr>\n  </tbody>\n</table>\n\n``` python\nsub_dsets = mnist.valid.random_sub_dsets(64)\nlearn.show_results(shuffle=False, dl=sub_dsets.dl())\n```\n\n![](index_files/figure-commonmark/cell-4-output-2.png)\n\n``` python\nattack = InputOptimizer(classifier, LinfPGD(epsilon=.15), n_epochs=10, epoch_size=20)\nperturbed_dsets = attack.perturb(sub_dsets)\n```\n\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>-4.302573</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>-7.585707</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>-9.014968</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>-9.700548</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>-10.075110</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>-10.296636</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>-10.433834</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>-10.521141</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>-10.577673</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>-10.614740</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>\n\n``` python\nlearn.show_results(shuffle=False, dl=TfmdDL(perturbed_dsets))\n```\n\n![](index_files/figure-commonmark/cell-6-output-2.png)\n\n### Data Poisoning\n\n``` python\npatch = torch.tensor([[1, 0, 1],\n                      [0, 1, 0],\n                      [1, 0, 1]]).int()*255\ntrigger = F.pad(patch, (25, 0, 25, 0)).numpy()\nlearn = Learner(mnist.dls(), MLP(10), metrics=accuracy, cbs=BadNetsAttack(trigger, '0'))\nlearn.fit_one_cycle(1)\n```\n\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>accuracy</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.103652</td>\n      <td>0.097075</td>\n      <td>0.971400</td>\n      <td>00:23</td>\n    </tr>\n  </tbody>\n</table>\n\nBenign performance:\n\n``` python\nlearn.show_results()\n```\n\n![](index_files/figure-commonmark/cell-8-output-2.png)\n\nAttack success:\n\n``` python\nlearn.show_results(2)\n```\n\n![](index_files/figure-commonmark/cell-9-output-2.png)\n\n\n",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/Irad-Zehavi/adv-ml",
        "keywords": "nbdev jupyter notebook python",
        "license": "Apache Software License 2.0",
        "maintainer": "",
        "maintainer_email": "",
        "name": "adv-ml",
        "package_url": "https://pypi.org/project/adv-ml/",
        "platform": null,
        "project_url": "https://pypi.org/project/adv-ml/",
        "project_urls": {
            "Homepage": "https://github.com/Irad-Zehavi/adv-ml"
        },
        "release_url": "https://pypi.org/project/adv-ml/0.0.4/",
        "requires_dist": [
            "fastai",
            "fastai-datasets",
            "similarity-learning",
            "nbdev ; extra == 'dev'"
        ],
        "requires_python": ">=3.7",
        "summary": "A modular and easy-to-use framework of adversarial machine learning algorithms: https://en.m.wikipedia.org/wiki/Adversarial_machine_learning",
        "version": "0.0.4",
        "yanked": false,
        "yanked_reason": null
    },
    "last_serial": 18449924,
    "releases": {
        "0.0.1": [
            {
                "comment_text": "",
                "digests": {
                    "blake2b_256": "e358851b04597078a195fa86c21e8993f24be61c5806c29f8847ca220d966f75",
                    "md5": "522d2d98677b2d999e09f946a58b40f1",
                    "sha256": "e47721f714802df504d2923bd6377e6a8810edce6a838623f252b7f98cedfacd"
                },
                "downloads": -1,
                "filename": "adv_ml-0.0.1-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "522d2d98677b2d999e09f946a58b40f1",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.7",
                "size": 16912,
                "upload_time": "2023-05-18T08:49:38",
                "upload_time_iso_8601": "2023-05-18T08:49:38.839147Z",
                "url": "https://files.pythonhosted.org/packages/e3/58/851b04597078a195fa86c21e8993f24be61c5806c29f8847ca220d966f75/adv_ml-0.0.1-py3-none-any.whl",
                "yanked": false,
                "yanked_reason": null
            },
            {
                "comment_text": "",
                "digests": {
                    "blake2b_256": "1bae82c6c8fa200e79662c020b15a2cb735cf78dc4b77c6ce4be823b46a4fc41",
                    "md5": "bc7f811d9461893e1b2ca34fef579838",
                    "sha256": "43663b05156777583424787bdcee5909320eb6e4fb55d76672c9e775a76f57e0"
                },
                "downloads": -1,
                "filename": "adv-ml-0.0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "bc7f811d9461893e1b2ca34fef579838",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.7",
                "size": 17595,
                "upload_time": "2023-05-18T08:49:41",
                "upload_time_iso_8601": "2023-05-18T08:49:41.218742Z",
                "url": "https://files.pythonhosted.org/packages/1b/ae/82c6c8fa200e79662c020b15a2cb735cf78dc4b77c6ce4be823b46a4fc41/adv-ml-0.0.1.tar.gz",
                "yanked": false,
                "yanked_reason": null
            }
        ],
        "0.0.2": [
            {
                "comment_text": "",
                "digests": {
                    "blake2b_256": "8250a9dece080a1f26727bb82dff841dae3d39601425ad234f28c9e46899788b",
                    "md5": "d0c7bb23a177ef414737652be5a54a2e",
                    "sha256": "9417aef643e665e594c03a5511b4bb1c26920fcb7eb262bb5239eb46a9a7b237"
                },
                "downloads": -1,
                "filename": "adv_ml-0.0.2-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "d0c7bb23a177ef414737652be5a54a2e",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.7",
                "size": 18533,
                "upload_time": "2023-05-18T14:34:09",
                "upload_time_iso_8601": "2023-05-18T14:34:09.262042Z",
                "url": "https://files.pythonhosted.org/packages/82/50/a9dece080a1f26727bb82dff841dae3d39601425ad234f28c9e46899788b/adv_ml-0.0.2-py3-none-any.whl",
                "yanked": false,
                "yanked_reason": null
            },
            {
                "comment_text": "",
                "digests": {
                    "blake2b_256": "6c143207da0513e00cf1078c8089d69ec611d0a5a64ee53adff1b5a1b85f2c63",
                    "md5": "f155f99fcaffef806bb4585700b149e1",
                    "sha256": "29d2558c4b88af68742a9242b5ada4643149765d2bb0fa3139566a1d9ea977ac"
                },
                "downloads": -1,
                "filename": "adv-ml-0.0.2.tar.gz",
                "has_sig": false,
                "md5_digest": "f155f99fcaffef806bb4585700b149e1",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.7",
                "size": 19870,
                "upload_time": "2023-05-18T14:34:12",
                "upload_time_iso_8601": "2023-05-18T14:34:12.746744Z",
                "url": "https://files.pythonhosted.org/packages/6c/14/3207da0513e00cf1078c8089d69ec611d0a5a64ee53adff1b5a1b85f2c63/adv-ml-0.0.2.tar.gz",
                "yanked": false,
                "yanked_reason": null
            }
        ],
        "0.0.4": [
            {
                "comment_text": "",
                "digests": {
                    "blake2b_256": "d8afb3dfb924c69cdb1084ce08107c4072f5e5e94414489803f8da6d7dddde4e",
                    "md5": "8ee434a5c26a9c81ebf7b553973f7112",
                    "sha256": "a1c284973cac35915fa50ad4ae513a7069b594ece6faeb32fda5e869ad0e013f"
                },
                "downloads": -1,
                "filename": "adv_ml-0.0.4-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "8ee434a5c26a9c81ebf7b553973f7112",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.7",
                "size": 20760,
                "upload_time": "2023-06-10T10:07:32",
                "upload_time_iso_8601": "2023-06-10T10:07:32.508540Z",
                "url": "https://files.pythonhosted.org/packages/d8/af/b3dfb924c69cdb1084ce08107c4072f5e5e94414489803f8da6d7dddde4e/adv_ml-0.0.4-py3-none-any.whl",
                "yanked": false,
                "yanked_reason": null
            },
            {
                "comment_text": "",
                "digests": {
                    "blake2b_256": "8d6208d24ee327042bf70bef28680a06253757222a0c24032da9ecdf98c171fa",
                    "md5": "2aaa95cf2e948b6a85c83879b8dc8a8e",
                    "sha256": "fb99423d52072d8e4e1919e8fd3f59c6f259c2e3b756bc38ea0849b9f0437927"
                },
                "downloads": -1,
                "filename": "adv-ml-0.0.4.tar.gz",
                "has_sig": false,
                "md5_digest": "2aaa95cf2e948b6a85c83879b8dc8a8e",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.7",
                "size": 21980,
                "upload_time": "2023-06-10T10:07:34",
                "upload_time_iso_8601": "2023-06-10T10:07:34.406069Z",
                "url": "https://files.pythonhosted.org/packages/8d/62/08d24ee327042bf70bef28680a06253757222a0c24032da9ecdf98c171fa/adv-ml-0.0.4.tar.gz",
                "yanked": false,
                "yanked_reason": null
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "d8afb3dfb924c69cdb1084ce08107c4072f5e5e94414489803f8da6d7dddde4e",
                "md5": "8ee434a5c26a9c81ebf7b553973f7112",
                "sha256": "a1c284973cac35915fa50ad4ae513a7069b594ece6faeb32fda5e869ad0e013f"
            },
            "downloads": -1,
            "filename": "adv_ml-0.0.4-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "8ee434a5c26a9c81ebf7b553973f7112",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.7",
            "size": 20760,
            "upload_time": "2023-06-10T10:07:32",
            "upload_time_iso_8601": "2023-06-10T10:07:32.508540Z",
            "url": "https://files.pythonhosted.org/packages/d8/af/b3dfb924c69cdb1084ce08107c4072f5e5e94414489803f8da6d7dddde4e/adv_ml-0.0.4-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "8d6208d24ee327042bf70bef28680a06253757222a0c24032da9ecdf98c171fa",
                "md5": "2aaa95cf2e948b6a85c83879b8dc8a8e",
                "sha256": "fb99423d52072d8e4e1919e8fd3f59c6f259c2e3b756bc38ea0849b9f0437927"
            },
            "downloads": -1,
            "filename": "adv-ml-0.0.4.tar.gz",
            "has_sig": false,
            "md5_digest": "2aaa95cf2e948b6a85c83879b8dc8a8e",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.7",
            "size": 21980,
            "upload_time": "2023-06-10T10:07:34",
            "upload_time_iso_8601": "2023-06-10T10:07:34.406069Z",
            "url": "https://files.pythonhosted.org/packages/8d/62/08d24ee327042bf70bef28680a06253757222a0c24032da9ecdf98c171fa/adv-ml-0.0.4.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "vulnerabilities": []
}