├── .gitignore ├── README.md ├── images ├── dog.jpg ├── fgsm_formula.png ├── funny_cat.jpg ├── ifgsm_formula.png ├── iterative_fgsm_total.jpg ├── iterative_fgsm_total.png ├── step_1_to_9.jpg ├── streamlit_app.gif └── subscribe.jpg ├── notebooks ├── end2end.ipynb └── end2end_old.ipynb ├── poetry.lock ├── pyproject.toml └── src ├── __init__.py ├── fgsm.py ├── imagenet_class_labels.py ├── model.py ├── streamlit_app.py └── viz.py /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .idea/ 3 | .venv/ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |
2 |

Breaking deep learning models with adversarial examples 😈

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Or maybe it's just that beautiful things are so easily broken by the world.

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-- Cassandra Clare, City of Fallen Angels

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6 | 7 | ![](http://datamachines.xyz/wp-content/uploads/2021/06/camaleon-2048x1524.jpg) 8 | 9 | ## Table of Contents 10 | 11 | 1. [What is this repo about?](#what-is-this-repo-about) 12 | 2. [Quick setup](#quick-setup) 13 | 3. [Fast Gradient Sign method](#fast-gradient-sign-method) 14 | 4. [Adversarial example generator](#adversarial-example-generator) 15 | 5. [Let's connect!](#lets-connect) 16 | 17 | 18 | ## What is this repo about? 19 | 20 | PyTorch code and [streamlit app](https://share.streamlit.io/paulescu/adversarial-machine-learning/main/src/streamlit_app.py) that demonstrate how easy it is to break deep learning models in computer vision. 21 | More precisely, Inception V3. 22 | 23 | If you do not know what adversarial examples are go check my blog post. 24 | 25 | [📝 Adversarial examples to break deep learning models](http://datamachines.xyz/2021/07/05/adversarial-examples-to-break-deep-learning-models/) 26 | 27 | Also available in [Medium](https://towardsdatascience.com/adversarial-examples-to-break-deep-learning-models-e7f543833eae) and in [Hackernoon](https://hackernoon.com/adversarial-examples-in-machine-learning-explained) 28 | 29 | ## Quick setup 30 | 31 | Create a virtualenv with your preferred tool (`virtualenv`, `conda`, `poetry`) 32 | and activate it. 33 | 34 | Then install the code as a local package 35 | ``` 36 | $ (venv) pip install . 37 | ``` 38 | 39 | 40 | ## Fast Gradient Sign method 41 | 42 | We use the vanilla fast gradient sign method 43 | 44 | ![](images/fgsm_formula.png) 45 | 46 | 47 | And its iterative version. 48 | 49 | ![](images/ifgsm_formula.png) 50 | 51 | 52 | In the code you can find the "magic" that transforms a nice puppy into a paper towel. 53 | 54 | ![](images/step_1_to_9.jpg) 55 | 56 | ## Adversarial example generator 57 | 58 | 👉 [Streamlit app to generate adversarial examples](https://share.streamlit.io/paulescu/adversarial-machine-learning/main/src/streamlit_app.py) 59 | ![](images/streamlit_app.gif) 60 | 61 | ## Let's connect 62 | 63 | If you want to learn more about real-world ML topics and become a better data scientist 64 | 65 | 👉 [Subscribe](http://datamachines.xyz/subscribe) to the datamachines newsletter. 66 | 67 | 👉🏽 Follow me on [Twitter](https://twitter.com/paulabartabajo_) and/or [LinkedIn](https://www.linkedin.com/in/pau-labarta-bajo-4432074b/) 68 | 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Install all the Jupyter components in one go." 476 | category = "main" 477 | optional = false 478 | python-versions = "*" 479 | 480 | [package.dependencies] 481 | ipykernel = "*" 482 | ipywidgets = "*" 483 | jupyter-console = "*" 484 | nbconvert = "*" 485 | notebook = "*" 486 | qtconsole = "*" 487 | 488 | [[package]] 489 | name = "jupyter-client" 490 | version = "6.2.0" 491 | description = "Jupyter protocol implementation and client libraries" 492 | category = "main" 493 | optional = false 494 | python-versions = ">=3.6.1" 495 | 496 | [package.dependencies] 497 | jupyter-core = ">=4.6.0" 498 | nest-asyncio = ">=1.5" 499 | python-dateutil = ">=2.1" 500 | pyzmq = ">=13" 501 | tornado = ">=4.1" 502 | traitlets = "*" 503 | 504 | [package.extras] 505 | doc = ["sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"] 506 | test = ["async-generator", "ipykernel", "ipython", "mock", "pytest-asyncio", "pytest-timeout", "pytest", "mypy", "pre-commit", "jedi (<0.18)"] 507 | 508 | [[package]] 509 | name = "jupyter-console" 510 | version = "6.4.0" 511 | description = "Jupyter terminal console" 512 | category = "main" 513 | optional = false 514 | python-versions = ">=3.6" 515 | 516 | [package.dependencies] 517 | ipykernel = "*" 518 | ipython = "*" 519 | jupyter-client = "*" 520 | prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0" 521 | pygments = "*" 522 | 523 | [package.extras] 524 | test = ["pexpect"] 525 | 526 | [[package]] 527 | name = "jupyter-core" 528 | version = "4.7.1" 529 | description = "Jupyter core package. 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Formerly nbconvert's ExecutePreprocessor." 631 | category = "main" 632 | optional = false 633 | python-versions = ">=3.6.1" 634 | 635 | [package.dependencies] 636 | async-generator = "*" 637 | jupyter-client = ">=6.1.5" 638 | nbformat = ">=5.0" 639 | nest-asyncio = "*" 640 | traitlets = ">=4.2" 641 | 642 | [package.extras] 643 | dev = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"] 644 | sphinx = ["Sphinx (>=1.7)", "sphinx-book-theme", "mock", "moto", "myst-parser"] 645 | test = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"] 646 | 647 | [[package]] 648 | name = "nbconvert" 649 | version = "6.1.0" 650 | description = "Converting Jupyter Notebooks" 651 | category = "main" 652 | optional = false 653 | python-versions = ">=3.7" 654 | 655 | [package.dependencies] 656 | bleach = "*" 657 | defusedxml = "*" 658 | entrypoints = ">=0.2.2" 659 | jinja2 = ">=2.4" 660 | jupyter-core = "*" 661 | jupyterlab-pygments = "*" 662 | mistune = ">=0.8.1,<2" 663 | nbclient = ">=0.5.0,<0.6.0" 664 | nbformat = ">=4.4" 665 | pandocfilters = ">=1.4.1" 666 | pygments = ">=2.4.1" 667 | testpath = "*" 668 | traitlets = ">=5.0" 669 | 670 | [package.extras] 671 | all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"] 672 | docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"] 673 | serve = ["tornado (>=4.0)"] 674 | test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)"] 675 | webpdf = ["pyppeteer (==0.2.2)"] 676 | 677 | [[package]] 678 | name = "nbformat" 679 | version = "5.1.3" 680 | description = "The Jupyter Notebook format" 681 | category = "main" 682 | optional = false 683 | python-versions = ">=3.5" 684 | 685 | [package.dependencies] 686 | ipython-genutils = "*" 687 | jsonschema = ">=2.4,<2.5.0 || >2.5.0" 688 | jupyter-core = "*" 689 | traitlets = ">=4.1" 690 | 691 | [package.extras] 692 | fast = ["fastjsonschema"] 693 | test = ["check-manifest", "fastjsonschema", "testpath", "pytest", "pytest-cov"] 694 | 695 | [[package]] 696 | name = "nest-asyncio" 697 | version = "1.5.1" 698 | description = "Patch asyncio to allow nested event loops" 699 | category = "main" 700 | optional = false 701 | python-versions = ">=3.5" 702 | 703 | [[package]] 704 | name = "nose" 705 | version = "1.3.7" 706 | description = "nose extends unittest to make testing easier" 707 | category = "main" 708 | optional = false 709 | python-versions = "*" 710 | 711 | [[package]] 712 | name = "notebook" 713 | version = "6.4.0" 714 | description = "A web-based notebook environment for interactive computing" 715 | category = "main" 716 | optional = false 717 | python-versions = ">=3.6" 718 | 719 | [package.dependencies] 720 | argon2-cffi = "*" 721 | ipykernel = "*" 722 | ipython-genutils = "*" 723 | jinja2 = "*" 724 | jupyter-client = ">=5.3.4" 725 | jupyter-core = ">=4.6.1" 726 | nbconvert = "*" 727 | nbformat = "*" 728 | prometheus-client = "*" 729 | pyzmq = ">=17" 730 | Send2Trash = ">=1.5.0" 731 | terminado = ">=0.8.3" 732 | tornado = ">=6.1" 733 | traitlets = ">=4.2.1" 734 | 735 | [package.extras] 736 | docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"] 737 | json-logging = ["json-logging"] 738 | test = ["pytest", "coverage", "requests", "nbval", "selenium", "pytest-cov", "requests-unixsocket"] 739 | 740 | [[package]] 741 | name = "numpy" 742 | version = "1.21.0" 743 | description = "NumPy is the fundamental package for array computing with Python." 744 | category = "main" 745 | optional = false 746 | python-versions = ">=3.7" 747 | 748 | [[package]] 749 | name = "packaging" 750 | version = "20.9" 751 | description = "Core utilities for Python packages" 752 | category = "main" 753 | optional = false 754 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 755 | 756 | [package.dependencies] 757 | pyparsing = ">=2.0.2" 758 | 759 | [[package]] 760 | name = "pandas" 761 | version = "1.1.5" 762 | description = "Powerful data structures for data analysis, time series, and statistics" 763 | category = "main" 764 | optional = false 765 | python-versions = ">=3.6.1" 766 | 767 | [package.dependencies] 768 | numpy = ">=1.15.4" 769 | python-dateutil = ">=2.7.3" 770 | pytz = ">=2017.2" 771 | 772 | [package.extras] 773 | test = ["pytest (>=4.0.2)", "pytest-xdist", "hypothesis (>=3.58)"] 774 | 775 | [[package]] 776 | name = "pandocfilters" 777 | version = "1.4.3" 778 | description = "Utilities for writing pandoc filters in python" 779 | category = "main" 780 | optional = false 781 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 782 | 783 | [[package]] 784 | name = "parso" 785 | version = "0.8.2" 786 | description = "A Python Parser" 787 | category = "main" 788 | optional = false 789 | python-versions = ">=3.6" 790 | 791 | [package.extras] 792 | qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] 793 | testing = ["docopt", "pytest (<6.0.0)"] 794 | 795 | [[package]] 796 | name = "pexpect" 797 | version = "4.8.0" 798 | description = "Pexpect allows easy control of interactive console applications." 799 | category = "main" 800 | optional = false 801 | python-versions = "*" 802 | 803 | [package.dependencies] 804 | ptyprocess = ">=0.5" 805 | 806 | [[package]] 807 | name = "pickleshare" 808 | version = "0.7.5" 809 | description = "Tiny 'shelve'-like database with concurrency support" 810 | category = "main" 811 | optional = false 812 | python-versions = "*" 813 | 814 | [[package]] 815 | name = "pillow" 816 | version = "8.2.0" 817 | description = "Python Imaging Library (Fork)" 818 | category = "main" 819 | optional = false 820 | python-versions = ">=3.6" 821 | 822 | [[package]] 823 | name = "pluggy" 824 | version = "0.13.1" 825 | description = "plugin and hook calling mechanisms for python" 826 | category = "dev" 827 | optional = false 828 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 829 | 830 | [package.dependencies] 831 | importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""} 832 | 833 | [package.extras] 834 | dev = ["pre-commit", "tox"] 835 | 836 | [[package]] 837 | name = "prometheus-client" 838 | version = "0.11.0" 839 | description = "Python client for the Prometheus monitoring system." 840 | category = "main" 841 | optional = false 842 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 843 | 844 | [package.extras] 845 | twisted = ["twisted"] 846 | 847 | [[package]] 848 | name = "prompt-toolkit" 849 | version = "3.0.19" 850 | description = "Library for building powerful interactive command lines in Python" 851 | category = "main" 852 | optional = false 853 | python-versions = ">=3.6.1" 854 | 855 | [package.dependencies] 856 | wcwidth = "*" 857 | 858 | [[package]] 859 | name = "protobuf" 860 | version = "3.19.4" 861 | description = "Protocol Buffers" 862 | category = "main" 863 | optional = false 864 | python-versions = ">=3.5" 865 | 866 | [[package]] 867 | name = "ptyprocess" 868 | version = "0.7.0" 869 | description = "Run a subprocess in a pseudo terminal" 870 | category = "main" 871 | optional = false 872 | python-versions = "*" 873 | 874 | [[package]] 875 | name = "py" 876 | version = "1.10.0" 877 | description = "library with cross-python path, ini-parsing, io, code, log facilities" 878 | category = "main" 879 | optional = false 880 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 881 | 882 | [[package]] 883 | name = "pyarrow" 884 | version = "7.0.0" 885 | description = "Python library for Apache Arrow" 886 | category = "main" 887 | optional = false 888 | python-versions = ">=3.7" 889 | 890 | [package.dependencies] 891 | numpy = ">=1.16.6" 892 | 893 | [[package]] 894 | name = "pycodestyle" 895 | version = "2.7.0" 896 | description = "Python style guide checker" 897 | category = "main" 898 | optional = false 899 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 900 | 901 | [[package]] 902 | name = "pycparser" 903 | version = "2.20" 904 | description = "C parser in Python" 905 | category = "main" 906 | optional = false 907 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 908 | 909 | [[package]] 910 | name = "pydeck" 911 | version = "0.7.1" 912 | description = "Widget for deck.gl maps" 913 | category = "main" 914 | optional = false 915 | python-versions = ">=3.7" 916 | 917 | [package.dependencies] 918 | ipykernel = {version = ">=5.1.2", markers = "python_version >= \"3.4\""} 919 | ipywidgets = ">=7.0.0" 920 | jinja2 = ">=2.10.1" 921 | numpy = ">=1.16.4" 922 | traitlets = ">=4.3.2" 923 | 924 | [package.extras] 925 | testing = ["pytest"] 926 | 927 | [[package]] 928 | name = "pygments" 929 | version = "2.9.0" 930 | description = "Pygments is a syntax highlighting package written in Python." 931 | category = "main" 932 | optional = false 933 | python-versions = ">=3.5" 934 | 935 | [[package]] 936 | name = "pympler" 937 | version = "1.0.1" 938 | description = "A development tool to measure, monitor and analyze the memory behavior of Python objects." 939 | category = "main" 940 | optional = false 941 | python-versions = ">=3.6" 942 | 943 | [[package]] 944 | name = "pyparsing" 945 | version = "2.4.7" 946 | description = "Python parsing module" 947 | category = "main" 948 | optional = false 949 | python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" 950 | 951 | [[package]] 952 | name = "pyrsistent" 953 | version = "0.17.3" 954 | description = "Persistent/Functional/Immutable data structures" 955 | category = "main" 956 | optional = false 957 | python-versions = ">=3.5" 958 | 959 | [[package]] 960 | name = "pytest" 961 | version = "5.4.3" 962 | description = "pytest: simple powerful testing with Python" 963 | category = "dev" 964 | optional = false 965 | python-versions = ">=3.5" 966 | 967 | [package.dependencies] 968 | atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""} 969 | attrs = ">=17.4.0" 970 | colorama = {version = "*", markers = "sys_platform == \"win32\""} 971 | importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""} 972 | more-itertools = ">=4.0.0" 973 | packaging = "*" 974 | pluggy = ">=0.12,<1.0" 975 | py = ">=1.5.0" 976 | wcwidth = "*" 977 | 978 | [package.extras] 979 | checkqa-mypy = ["mypy (==v0.761)"] 980 | testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] 981 | 982 | [[package]] 983 | name = "python-dateutil" 984 | version = "2.8.1" 985 | description = "Extensions to the standard Python datetime module" 986 | category = "main" 987 | optional = false 988 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" 989 | 990 | [package.dependencies] 991 | six = ">=1.5" 992 | 993 | [[package]] 994 | name = "pytz" 995 | version = "2021.3" 996 | description = "World timezone definitions, modern and historical" 997 | category = "main" 998 | optional = false 999 | python-versions = "*" 1000 | 1001 | [[package]] 1002 | name = "pytz-deprecation-shim" 1003 | version = "0.1.0.post0" 1004 | description = "Shims to make deprecation of pytz easier" 1005 | category = "main" 1006 | optional = false 1007 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" 1008 | 1009 | [package.dependencies] 1010 | "backports.zoneinfo" = {version = "*", markers = "python_version >= \"3.6\" and python_version < \"3.9\""} 1011 | tzdata = {version = "*", markers = "python_version >= \"3.6\""} 1012 | 1013 | [[package]] 1014 | name = "pywin32" 1015 | version = "301" 1016 | description = "Python for Window Extensions" 1017 | category = "main" 1018 | optional = false 1019 | python-versions = "*" 1020 | 1021 | [[package]] 1022 | name = "pywinpty" 1023 | version = "1.1.3" 1024 | description = "Pseudo terminal support for Windows from Python." 1025 | category = "main" 1026 | optional = false 1027 | python-versions = ">=3.6" 1028 | 1029 | [[package]] 1030 | name = "pyzmq" 1031 | version = "22.1.0" 1032 | description = "Python bindings for 0MQ" 1033 | category = "main" 1034 | optional = false 1035 | python-versions = ">=3.6" 1036 | 1037 | [package.dependencies] 1038 | cffi = {version = "*", markers = "implementation_name == \"pypy\""} 1039 | py = {version = "*", markers = "implementation_name == \"pypy\""} 1040 | 1041 | [[package]] 1042 | name = "qtconsole" 1043 | version = "5.1.0" 1044 | description = "Jupyter Qt console" 1045 | category = "main" 1046 | optional = false 1047 | python-versions = ">= 3.6" 1048 | 1049 | [package.dependencies] 1050 | ipykernel = ">=4.1" 1051 | ipython-genutils = "*" 1052 | jupyter-client = ">=4.1" 1053 | jupyter-core = "*" 1054 | pygments = "*" 1055 | pyzmq = ">=17.1" 1056 | qtpy = "*" 1057 | traitlets = "*" 1058 | 1059 | [package.extras] 1060 | doc = ["Sphinx (>=1.3)"] 1061 | test = ["flaky", "pytest", "pytest-qt"] 1062 | 1063 | [[package]] 1064 | name = "qtpy" 1065 | version = "1.9.0" 1066 | description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5, PyQt4 and PySide) and additional custom QWidgets." 1067 | category = "main" 1068 | optional = false 1069 | python-versions = "*" 1070 | 1071 | [[package]] 1072 | name = "requests" 1073 | version = "2.25.1" 1074 | description = "Python HTTP for Humans." 1075 | category = "main" 1076 | optional = false 1077 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 1078 | 1079 | [package.dependencies] 1080 | certifi = ">=2017.4.17" 1081 | chardet = ">=3.0.2,<5" 1082 | idna = ">=2.5,<3" 1083 | urllib3 = ">=1.21.1,<1.27" 1084 | 1085 | [package.extras] 1086 | security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)"] 1087 | socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"] 1088 | 1089 | [[package]] 1090 | name = "scipy" 1091 | version = "1.6.1" 1092 | description = "SciPy: Scientific Library for Python" 1093 | category = "main" 1094 | optional = false 1095 | python-versions = ">=3.7" 1096 | 1097 | [package.dependencies] 1098 | numpy = ">=1.16.5" 1099 | 1100 | [[package]] 1101 | name = "send2trash" 1102 | version = "1.7.1" 1103 | description = "Send file to trash natively under Mac OS X, Windows and Linux." 1104 | category = "main" 1105 | optional = false 1106 | python-versions = "*" 1107 | 1108 | [package.extras] 1109 | win32 = ["pywin32"] 1110 | 1111 | [[package]] 1112 | name = "six" 1113 | version = "1.16.0" 1114 | description = "Python 2 and 3 compatibility utilities" 1115 | category = "main" 1116 | optional = false 1117 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" 1118 | 1119 | [[package]] 1120 | name = "smmap" 1121 | version = "5.0.0" 1122 | description = "A pure Python implementation of a sliding window memory map manager" 1123 | category = "main" 1124 | optional = false 1125 | python-versions = ">=3.6" 1126 | 1127 | [[package]] 1128 | name = "streamlit" 1129 | version = "1.5.1" 1130 | description = "The fastest way to build data apps in Python" 1131 | category = "main" 1132 | optional = false 1133 | python-versions = ">=3.6" 1134 | 1135 | [package.dependencies] 1136 | altair = ">=3.2.0" 1137 | astor = "*" 1138 | attrs = "*" 1139 | base58 = "*" 1140 | blinker = "*" 1141 | cachetools = ">=4.0" 1142 | click = ">=7.0" 1143 | gitpython = "!=3.1.19" 1144 | numpy = "*" 1145 | packaging = "*" 1146 | pandas = ">=0.21.0" 1147 | pillow = ">=6.2.0" 1148 | protobuf = ">=3.6.0,<3.11 || >3.11" 1149 | pyarrow = "*" 1150 | pydeck = ">=0.1.dev5" 1151 | pympler = ">=0.9" 1152 | python-dateutil = "*" 1153 | requests = "*" 1154 | toml = 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easydict = "^1.9" 14 | jupyter = "^1.0.0" 15 | requests = "^2.25.1" 16 | streamlit = "^1.5.1" 17 | 18 | [tool.poetry.dev-dependencies] 19 | pytest = "^5.2" 20 | 21 | [build-system] 22 | requires = ["poetry>=0.12"] 23 | build-backend = "poetry.masonry.api" 24 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | __version__ = '0.1.0' 2 | -------------------------------------------------------------------------------- /src/fgsm.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple, List, Optional 2 | 3 | import torch 4 | from torch.autograd import Variable 5 | import torch.nn as nn 6 | 7 | from src.model import inverse_preprocess 8 | 9 | 10 | def fast_gradient_sign( 11 | model: nn.Module, 12 | x: torch.Tensor, 13 | eps: float, 14 | output_type: Optional[str] = 'tensor' 15 | ) -> Tuple[torch.Tensor, torch.Tensor]: 16 | """""" 17 | assert output_type in {'tensor', 'rgb'}, 'Invalid output_type!' 18 | 19 | # tensor to variable so we can compute gradients with respect to it. 20 | img_variable = Variable(x, requires_grad=True) 21 | 22 | # forward pass on the original image 23 | output = model.forward(img_variable) 24 | 25 | # predicted class 26 | y_true = torch.max(output.data, 1)[1][0].item() 27 | target = Variable(torch.LongTensor([y_true]), requires_grad=False) 28 | 29 | # loss 30 | loss_fn = torch.nn.CrossEntropyLoss() 31 | loss = loss_fn(output, target) 32 | 33 | # compute gradient wrt each variable (requires_grad=True) 34 | # which you can later access with "var.grad.data" 35 | loss.backward(retain_graph=True) 36 | 37 | # sign of the gradient wrt input image 38 | x_grad = torch.sign(img_variable.grad.data) 39 | 40 | # FGSM 41 | x_adversarial = img_variable.data + eps * x_grad 42 | 43 | if output_type == 'tensor': 44 | return x_adversarial, x_grad 45 | else: 46 | return inverse_preprocess(x_adversarial), inverse_preprocess(x_grad) 47 | 48 | def iterative_fast_gradient_sign( 49 | model: nn.Module, 50 | x_: torch.Tensor, 51 | epsilon, 52 | n_steps: int, 53 | alpha: float 54 | # ) -> List[Tuple[torch.Tensor, torch.Tensor]]: 55 | ) -> Tuple[torch.Tensor, torch.Tensor]: 56 | """""" 57 | x = x_.clone().detach() 58 | 59 | for step in range(n_steps): 60 | 61 | # one step using basic FGSM 62 | x_adv, grad = fast_gradient_sign(model, x, alpha) 63 | 64 | # total perturbation 65 | total_grad = x_adv - x_ 66 | 67 | # force total perturbation to be lower than epsilon in 68 | # absolute value 69 | total_grad = torch.clamp(total_grad, -epsilon, epsilon) 70 | 71 | # add total perturbation to the original image 72 | x_adv = x_ + total_grad 73 | 74 | print('Step ', step + 1) 75 | # visualize(x, x_adv, grad, eps) 76 | 77 | x = x_adv 78 | 79 | return x_adv, total_grad 80 | 81 | 82 | def iterative_fast_gradient_sign_( 83 | model: nn.Module, 84 | x_: torch.Tensor, 85 | epsilon, 86 | n_steps: int, 87 | alpha: float 88 | # ) -> List[Tuple[torch.Tensor, torch.Tensor]]: 89 | ) -> Tuple[torch.Tensor, torch.Tensor]: 90 | """""" 91 | x = x_.clone().detach() 92 | 93 | for step in range(n_steps): 94 | 95 | # one step using basic FGSM 96 | x_adv, grad = fast_gradient_sign(model, x, alpha) 97 | 98 | # total perturbation 99 | total_grad = x_adv - x_ 100 | 101 | # force total perturbation to be lower than epsilon in 102 | # absolute value 103 | total_grad = torch.clamp(total_grad, -epsilon, epsilon) 104 | 105 | # add total perturbation to the original image 106 | x_adv = x_ + total_grad 107 | 108 | x = x_adv 109 | 110 | yield inverse_preprocess(x_adv), inverse_preprocess(grad) 111 | -------------------------------------------------------------------------------- /src/imagenet_class_labels.py: -------------------------------------------------------------------------------- 1 | id2label = { 2 | 0: 'tench', 3 | 1: 'goldfish', 4 | 2: 'great white shark', 5 | 3: 'tiger shark', 6 | 4: 'hammerhead', 7 | 5: 'electric ray', 8 | 6: 'stingray', 9 | 7: 'cock', 10 | 8: 'hen', 11 | 9: 'ostrich', 12 | 10: 'brambling', 13 | 11: 'goldfinch', 14 | 12: 'house finch', 15 | 13: 'junco', 16 | 14: 'indigo bunting', 17 | 15: 'robin', 18 | 16: 'bulbul', 19 | 17: 'jay', 20 | 18: 'magpie', 21 | 19: 'chickadee', 22 | 20: 'water ouzel', 23 | 21: 'kite', 24 | 22: 'bald eagle', 25 | 23: 'vulture', 26 | 24: 'great grey owl', 27 | 25: 'European fire salamander', 28 | 26: 'common newt', 29 | 27: 'eft', 30 | 28: 'spotted salamander', 31 | 29: 'axolotl', 32 | 30: 'bullfrog', 33 | 31: 'tree frog', 34 | 32: 'tailed frog', 35 | 33: 'loggerhead', 36 | 34: 'leatherback turtle', 37 | 35: 'mud turtle', 38 | 36: 'terrapin', 39 | 37: 'box turtle', 40 | 38: 'banded gecko', 41 | 39: 'common iguana', 42 | 40: 'American chameleon', 43 | 41: 'whiptail', 44 | 42: 'agama', 45 | 43: 'frilled lizard', 46 | 44: 'alligator lizard', 47 | 45: 'Gila monster', 48 | 46: 'green lizard', 49 | 47: 'African chameleon', 50 | 48: 'Komodo dragon', 51 | 49: 'African crocodile', 52 | 50: 'American alligator', 53 | 51: 'triceratops', 54 | 52: 'thunder snake', 55 | 53: 'ringneck snake', 56 | 54: 'hognose snake', 57 | 55: 'green snake', 58 | 56: 'king snake', 59 | 57: 'garter snake', 60 | 58: 'water snake', 61 | 59: 'vine snake', 62 | 60: 'night snake', 63 | 61: 'boa constrictor', 64 | 62: 'rock python', 65 | 63: 'Indian cobra', 66 | 64: 'green mamba', 67 | 65: 'sea snake', 68 | 66: 'horned viper', 69 | 67: 'diamondback', 70 | 68: 'sidewinder', 71 | 69: 'trilobite', 72 | 70: 'harvestman', 73 | 71: 'scorpion', 74 | 72: 'black and gold garden spider', 75 | 73: 'barn spider', 76 | 74: 'garden spider', 77 | 75: 'black widow', 78 | 76: 'tarantula', 79 | 77: 'wolf spider', 80 | 78: 'tick', 81 | 79: 'centipede', 82 | 80: 'black grouse', 83 | 81: 'ptarmigan', 84 | 82: 'ruffed grouse', 85 | 83: 'prairie chicken', 86 | 84: 'peacock', 87 | 85: 'quail', 88 | 86: 'partridge', 89 | 87: 'African grey', 90 | 88: 'macaw', 91 | 89: 'sulphur-crested cockatoo', 92 | 90: 'lorikeet', 93 | 91: 'coucal', 94 | 92: 'bee eater', 95 | 93: 'hornbill', 96 | 94: 'hummingbird', 97 | 95: 'jacamar', 98 | 96: 'toucan', 99 | 97: 'drake', 100 | 98: 'red-breasted merganser', 101 | 99: 'goose', 102 | 100: 'black swan', 103 | 101: 'tusker', 104 | 102: 'echidna', 105 | 103: 'platypus', 106 | 104: 'wallaby', 107 | 105: 'koala', 108 | 106: 'wombat', 109 | 107: 'jellyfish', 110 | 108: 'sea anemone', 111 | 109: 'brain coral', 112 | 110: 'flatworm', 113 | 111: 'nematode', 114 | 112: 'conch', 115 | 113: 'snail', 116 | 114: 'slug', 117 | 115: 'sea slug', 118 | 116: 'chiton', 119 | 117: 'chambered nautilus', 120 | 118: 'Dungeness crab', 121 | 119: 'rock crab', 122 | 120: 'fiddler crab', 123 | 121: 'king crab', 124 | 122: 'American lobster', 125 | 123: 'spiny lobster', 126 | 124: 'crayfish', 127 | 125: 'hermit crab', 128 | 126: 'isopod', 129 | 127: 'white stork', 130 | 128: 'black stork', 131 | 129: 'spoonbill', 132 | 130: 'flamingo', 133 | 131: 'little blue heron', 134 | 132: 'American egret', 135 | 133: 'bittern', 136 | 134: 'crane', 137 | 135: 'limpkin', 138 | 136: 'European gallinule', 139 | 137: 'American coot', 140 | 138: 'bustard', 141 | 139: 'ruddy turnstone', 142 | 140: 'red-backed sandpiper', 143 | 141: 'redshank', 144 | 142: 'dowitcher', 145 | 143: 'oystercatcher', 146 | 144: 'pelican', 147 | 145: 'king penguin', 148 | 146: 'albatross', 149 | 147: 'grey whale', 150 | 148: 'killer whale', 151 | 149: 'dugong', 152 | 150: 'sea lion', 153 | 151: 'Chihuahua', 154 | 152: 'Japanese spaniel', 155 | 153: 'Maltese dog', 156 | 154: 'Pekinese', 157 | 155: 'Shih-Tzu', 158 | 156: 'Blenheim spaniel', 159 | 157: 'papillon', 160 | 158: 'toy terrier', 161 | 159: 'Rhodesian ridgeback', 162 | 160: 'Afghan hound', 163 | 161: 'basset', 164 | 162: 'beagle', 165 | 163: 'bloodhound', 166 | 164: 'bluetick', 167 | 165: 'black-and-tan coonhound', 168 | 166: 'Walker hound', 169 | 167: 'English foxhound', 170 | 168: 'redbone', 171 | 169: 'borzoi', 172 | 170: 'Irish wolfhound', 173 | 171: 'Italian greyhound', 174 | 172: 'whippet', 175 | 173: 'Ibizan hound', 176 | 174: 'Norwegian elkhound', 177 | 175: 'otterhound', 178 | 176: 'Saluki', 179 | 177: 'Scottish deerhound', 180 | 178: 'Weimaraner', 181 | 179: 'Staffordshire bullterrier', 182 | 180: 'American Staffordshire terrier', 183 | 181: 'Bedlington terrier', 184 | 182: 'Border terrier', 185 | 183: 'Kerry blue terrier', 186 | 184: 'Irish terrier', 187 | 185: 'Norfolk terrier', 188 | 186: 'Norwich terrier', 189 | 187: 'Yorkshire terrier', 190 | 188: 'wire-haired fox terrier', 191 | 189: 'Lakeland terrier', 192 | 190: 'Sealyham terrier', 193 | 191: 'Airedale', 194 | 192: 'cairn', 195 | 193: 'Australian terrier', 196 | 194: 'Dandie Dinmont', 197 | 195: 'Boston bull', 198 | 196: 'miniature schnauzer', 199 | 197: 'giant schnauzer', 200 | 198: 'standard schnauzer', 201 | 199: 'Scotch terrier', 202 | 200: 'Tibetan terrier', 203 | 201: 'silky terrier', 204 | 202: 'soft-coated wheaten terrier', 205 | 203: 'West Highland white terrier', 206 | 204: 'Lhasa', 207 | 205: 'flat-coated retriever', 208 | 206: 'curly-coated retriever', 209 | 207: 'golden retriever', 210 | 208: 'Labrador retriever', 211 | 209: 'Chesapeake Bay retriever', 212 | 210: 'German short-haired pointer', 213 | 211: 'vizsla', 214 | 212: 'English setter', 215 | 213: 'Irish setter', 216 | 214: 'Gordon setter', 217 | 215: 'Brittany spaniel', 218 | 216: 'clumber', 219 | 217: 'English springer', 220 | 218: 'Welsh springer spaniel', 221 | 219: 'cocker spaniel', 222 | 220: 'Sussex spaniel', 223 | 221: 'Irish water spaniel', 224 | 222: 'kuvasz', 225 | 223: 'schipperke', 226 | 224: 'groenendael', 227 | 225: 'malinois', 228 | 226: 'briard', 229 | 227: 'kelpie', 230 | 228: 'komondor', 231 | 229: 'Old English sheepdog', 232 | 230: 'Shetland sheepdog', 233 | 231: 'collie', 234 | 232: 'Border collie', 235 | 233: 'Bouvier des Flandres', 236 | 234: 'Rottweiler', 237 | 235: 'German shepherd', 238 | 236: 'Doberman', 239 | 237: 'miniature pinscher', 240 | 238: 'Greater Swiss Mountain dog', 241 | 239: 'Bernese mountain dog', 242 | 240: 'Appenzeller', 243 | 241: 'EntleBucher', 244 | 242: 'boxer', 245 | 243: 'bull mastiff', 246 | 244: 'Tibetan mastiff', 247 | 245: 'French bulldog', 248 | 246: 'Great Dane', 249 | 247: 'Saint Bernard', 250 | 248: 'Eskimo dog', 251 | 249: 'malamute', 252 | 250: 'Siberian husky', 253 | 251: 'dalmatian', 254 | 252: 'affenpinscher', 255 | 253: 'basenji', 256 | 254: 'pug', 257 | 255: 'Leonberg', 258 | 256: 'Newfoundland', 259 | 257: 'Great Pyrenees', 260 | 258: 'Samoyed', 261 | 259: 'Pomeranian', 262 | 260: 'chow', 263 | 261: 'keeshond', 264 | 262: 'Brabancon griffon', 265 | 263: 'Pembroke', 266 | 264: 'Cardigan', 267 | 265: 'toy poodle', 268 | 266: 'miniature poodle', 269 | 267: 'standard poodle', 270 | 268: 'Mexican hairless', 271 | 269: 'timber wolf', 272 | 270: 'white wolf', 273 | 271: 'red wolf', 274 | 272: 'coyote', 275 | 273: 'dingo', 276 | 274: 'dhole', 277 | 275: 'African hunting dog', 278 | 276: 'hyena', 279 | 277: 'red fox', 280 | 278: 'kit fox', 281 | 279: 'Arctic fox', 282 | 280: 'grey fox', 283 | 281: 'tabby', 284 | 282: 'tiger cat', 285 | 283: 'Persian cat', 286 | 284: 'Siamese cat', 287 | 285: 'Egyptian cat', 288 | 286: 'cougar', 289 | 287: 'lynx', 290 | 288: 'leopard', 291 | 289: 'snow leopard', 292 | 290: 'jaguar', 293 | 291: 'lion', 294 | 292: 'tiger', 295 | 293: 'cheetah', 296 | 294: 'brown bear', 297 | 295: 'American black bear', 298 | 296: 'ice bear', 299 | 297: 'sloth bear', 300 | 298: 'mongoose', 301 | 299: 'meerkat', 302 | 300: 'tiger beetle', 303 | 301: 'ladybug', 304 | 302: 'ground beetle', 305 | 303: 'long-horned beetle', 306 | 304: 'leaf beetle', 307 | 305: 'dung beetle', 308 | 306: 'rhinoceros beetle', 309 | 307: 'weevil', 310 | 308: 'fly', 311 | 309: 'bee', 312 | 310: 'ant', 313 | 311: 'grasshopper', 314 | 312: 'cricket', 315 | 313: 'walking stick', 316 | 314: 'cockroach', 317 | 315: 'mantis', 318 | 316: 'cicada', 319 | 317: 'leafhopper', 320 | 318: 'lacewing', 321 | 319: 'dragonfly', 322 | 320: 'damselfly', 323 | 321: 'admiral', 324 | 322: 'ringlet', 325 | 323: 'monarch', 326 | 324: 'cabbage butterfly', 327 | 325: 'sulphur butterfly', 328 | 326: 'lycaenid', 329 | 327: 'starfish', 330 | 328: 'sea urchin', 331 | 329: 'sea cucumber', 332 | 330: 'wood rabbit', 333 | 331: 'hare', 334 | 332: 'Angora', 335 | 333: 'hamster', 336 | 334: 'porcupine', 337 | 335: 'fox squirrel', 338 | 336: 'marmot', 339 | 337: 'beaver', 340 | 338: 'guinea pig', 341 | 339: 'sorrel', 342 | 340: 'zebra', 343 | 341: 'hog', 344 | 342: 'wild boar', 345 | 343: 'warthog', 346 | 344: 'hippopotamus', 347 | 345: 'ox', 348 | 346: 'water buffalo', 349 | 347: 'bison', 350 | 348: 'ram', 351 | 349: 'bighorn', 352 | 350: 'ibex', 353 | 351: 'hartebeest', 354 | 352: 'impala', 355 | 353: 'gazelle', 356 | 354: 'Arabian camel', 357 | 355: 'llama', 358 | 356: 'weasel', 359 | 357: 'mink', 360 | 358: 'polecat', 361 | 359: 'black-footed ferret', 362 | 360: 'otter', 363 | 361: 'skunk', 364 | 362: 'badger', 365 | 363: 'armadillo', 366 | 364: 'three-toed sloth', 367 | 365: 'orangutan', 368 | 366: 'gorilla', 369 | 367: 'chimpanzee', 370 | 368: 'gibbon', 371 | 369: 'siamang', 372 | 370: 'guenon', 373 | 371: 'patas', 374 | 372: 'baboon', 375 | 373: 'macaque', 376 | 374: 'langur', 377 | 375: 'colobus', 378 | 376: 'proboscis monkey', 379 | 377: 'marmoset', 380 | 378: 'capuchin', 381 | 379: 'howler monkey', 382 | 380: 'titi', 383 | 381: 'spider monkey', 384 | 382: 'squirrel monkey', 385 | 383: 'Madagascar cat', 386 | 384: 'indri', 387 | 385: 'Indian elephant', 388 | 386: 'African elephant', 389 | 387: 'lesser panda', 390 | 388: 'giant panda', 391 | 389: 'barracouta', 392 | 390: 'eel', 393 | 391: 'coho', 394 | 392: 'rock beauty', 395 | 393: 'anemone fish', 396 | 394: 'sturgeon', 397 | 395: 'gar', 398 | 396: 'lionfish', 399 | 397: 'puffer', 400 | 398: 'abacus', 401 | 399: 'abaya', 402 | 400: 'academic gown', 403 | 401: 'accordion', 404 | 402: 'acoustic guitar', 405 | 403: 'aircraft carrier', 406 | 404: 'airliner', 407 | 405: 'airship', 408 | 406: 'altar', 409 | 407: 'ambulance', 410 | 408: 'amphibian', 411 | 409: 'analog clock', 412 | 410: 'apiary', 413 | 411: 'apron', 414 | 412: 'ashcan', 415 | 413: 'assault rifle', 416 | 414: 'backpack', 417 | 415: 'bakery', 418 | 416: 'balance beam', 419 | 417: 'balloon', 420 | 418: 'ballpoint', 421 | 419: 'Band Aid', 422 | 420: 'banjo', 423 | 421: 'bannister', 424 | 422: 'barbell', 425 | 423: 'barber chair', 426 | 424: 'barbershop', 427 | 425: 'barn', 428 | 426: 'barometer', 429 | 427: 'barrel', 430 | 428: 'barrow', 431 | 429: 'baseball', 432 | 430: 'basketball', 433 | 431: 'bassinet', 434 | 432: 'bassoon', 435 | 433: 'bathing cap', 436 | 434: 'bath towel', 437 | 435: 'bathtub', 438 | 436: 'beach wagon', 439 | 437: 'beacon', 440 | 438: 'beaker', 441 | 439: 'bearskin', 442 | 440: 'beer bottle', 443 | 441: 'beer glass', 444 | 442: 'bell cote', 445 | 443: 'bib', 446 | 444: 'bicycle-built-for-two', 447 | 445: 'bikini', 448 | 446: 'binder', 449 | 447: 'binoculars', 450 | 448: 'birdhouse', 451 | 449: 'boathouse', 452 | 450: 'bobsled', 453 | 451: 'bolo tie', 454 | 452: 'bonnet', 455 | 453: 'bookcase', 456 | 454: 'bookshop', 457 | 455: 'bottlecap', 458 | 456: 'bow', 459 | 457: 'bow tie', 460 | 458: 'brass', 461 | 459: 'brassiere', 462 | 460: 'breakwater', 463 | 461: 'breastplate', 464 | 462: 'broom', 465 | 463: 'bucket', 466 | 464: 'buckle', 467 | 465: 'bulletproof vest', 468 | 466: 'bullet train', 469 | 467: 'butcher shop', 470 | 468: 'cab', 471 | 469: 'caldron', 472 | 470: 'candle', 473 | 471: 'cannon', 474 | 472: 'canoe', 475 | 473: 'can opener', 476 | 474: 'cardigan', 477 | 475: 'car mirror', 478 | 476: 'carousel', 479 | 477: "carpenter's kit", 480 | 478: 'carton', 481 | 479: 'car wheel', 482 | 480: 'cash machine', 483 | 481: 'cassette', 484 | 482: 'cassette player', 485 | 483: 'castle', 486 | 484: 'catamaran', 487 | 485: 'CD player', 488 | 486: 'cello', 489 | 487: 'cellular telephone', 490 | 488: 'chain', 491 | 489: 'chainlink fence', 492 | 490: 'chain mail', 493 | 491: 'chain saw', 494 | 492: 'chest', 495 | 493: 'chiffonier', 496 | 494: 'chime', 497 | 495: 'china cabinet', 498 | 496: 'Christmas stocking', 499 | 497: 'church', 500 | 498: 'cinema', 501 | 499: 'cleaver', 502 | 500: 'cliff dwelling', 503 | 501: 'cloak', 504 | 502: 'clog', 505 | 503: 'cocktail shaker', 506 | 504: 'coffee mug', 507 | 505: 'coffeepot', 508 | 506: 'coil', 509 | 507: 'combination lock', 510 | 508: 'computer keyboard', 511 | 509: 'confectionery', 512 | 510: 'container ship', 513 | 511: 'convertible', 514 | 512: 'corkscrew', 515 | 513: 'cornet', 516 | 514: 'cowboy boot', 517 | 515: 'cowboy hat', 518 | 516: 'cradle', 519 | 517: 'crane', 520 | 518: 'crash helmet', 521 | 519: 'crate', 522 | 520: 'crib', 523 | 521: 'Crock Pot', 524 | 522: 'croquet ball', 525 | 523: 'crutch', 526 | 524: 'cuirass', 527 | 525: 'dam', 528 | 526: 'desk', 529 | 527: 'desktop computer', 530 | 528: 'dial telephone', 531 | 529: 'diaper', 532 | 530: 'digital clock', 533 | 531: 'digital watch', 534 | 532: 'dining table', 535 | 533: 'dishrag', 536 | 534: 'dishwasher', 537 | 535: 'disk brake', 538 | 536: 'dock', 539 | 537: 'dogsled', 540 | 538: 'dome', 541 | 539: 'doormat', 542 | 540: 'drilling platform', 543 | 541: 'drum', 544 | 542: 'drumstick', 545 | 543: 'dumbbell', 546 | 544: 'Dutch oven', 547 | 545: 'electric fan', 548 | 546: 'electric guitar', 549 | 547: 'electric locomotive', 550 | 548: 'entertainment center', 551 | 549: 'envelope', 552 | 550: 'espresso maker', 553 | 551: 'face powder', 554 | 552: 'feather boa', 555 | 553: 'file', 556 | 554: 'fireboat', 557 | 555: 'fire engine', 558 | 556: 'fire screen', 559 | 557: 'flagpole', 560 | 558: 'flute', 561 | 559: 'folding chair', 562 | 560: 'football helmet', 563 | 561: 'forklift', 564 | 562: 'fountain', 565 | 563: 'fountain pen', 566 | 564: 'four-poster', 567 | 565: 'freight car', 568 | 566: 'French horn', 569 | 567: 'frying pan', 570 | 568: 'fur coat', 571 | 569: 'garbage truck', 572 | 570: 'gasmask', 573 | 571: 'gas pump', 574 | 572: 'goblet', 575 | 573: 'go-kart', 576 | 574: 'golf ball', 577 | 575: 'golfcart', 578 | 576: 'gondola', 579 | 577: 'gong', 580 | 578: 'gown', 581 | 579: 'grand piano', 582 | 580: 'greenhouse', 583 | 581: 'grille', 584 | 582: 'grocery store', 585 | 583: 'guillotine', 586 | 584: 'hair slide', 587 | 585: 'hair spray', 588 | 586: 'half track', 589 | 587: 'hammer', 590 | 588: 'hamper', 591 | 589: 'hand blower', 592 | 590: 'hand-held computer', 593 | 591: 'handkerchief', 594 | 592: 'hard disc', 595 | 593: 'harmonica', 596 | 594: 'harp', 597 | 595: 'harvester', 598 | 596: 'hatchet', 599 | 597: 'holster', 600 | 598: 'home theater', 601 | 599: 'honeycomb', 602 | 600: 'hook', 603 | 601: 'hoopskirt', 604 | 602: 'horizontal bar', 605 | 603: 'horse cart', 606 | 604: 'hourglass', 607 | 605: 'iPod', 608 | 606: 'iron', 609 | 607: "jack-o'-lantern", 610 | 608: 'jean', 611 | 609: 'jeep', 612 | 610: 'jersey', 613 | 611: 'jigsaw puzzle', 614 | 612: 'jinrikisha', 615 | 613: 'joystick', 616 | 614: 'kimono', 617 | 615: 'knee pad', 618 | 616: 'knot', 619 | 617: 'lab coat', 620 | 618: 'ladle', 621 | 619: 'lampshade', 622 | 620: 'laptop', 623 | 621: 'lawn mower', 624 | 622: 'lens cap', 625 | 623: 'letter opener', 626 | 624: 'library', 627 | 625: 'lifeboat', 628 | 626: 'lighter', 629 | 627: 'limousine', 630 | 628: 'liner', 631 | 629: 'lipstick', 632 | 630: 'Loafer', 633 | 631: 'lotion', 634 | 632: 'loudspeaker', 635 | 633: 'loupe', 636 | 634: 'lumbermill', 637 | 635: 'magnetic compass', 638 | 636: 'mailbag', 639 | 637: 'mailbox', 640 | 638: 'maillot', 641 | 639: 'maillot', 642 | 640: 'manhole cover', 643 | 641: 'maraca', 644 | 642: 'marimba', 645 | 643: 'mask', 646 | 644: 'matchstick', 647 | 645: 'maypole', 648 | 646: 'maze', 649 | 647: 'measuring cup', 650 | 648: 'medicine chest', 651 | 649: 'megalith', 652 | 650: 'microphone', 653 | 651: 'microwave', 654 | 652: 'military uniform', 655 | 653: 'milk can', 656 | 654: 'minibus', 657 | 655: 'miniskirt', 658 | 656: 'minivan', 659 | 657: 'missile', 660 | 658: 'mitten', 661 | 659: 'mixing bowl', 662 | 660: 'mobile home', 663 | 661: 'Model T', 664 | 662: 'modem', 665 | 663: 'monastery', 666 | 664: 'monitor', 667 | 665: 'moped', 668 | 666: 'mortar', 669 | 667: 'mortarboard', 670 | 668: 'mosque', 671 | 669: 'mosquito net', 672 | 670: 'motor scooter', 673 | 671: 'mountain bike', 674 | 672: 'mountain tent', 675 | 673: 'mouse', 676 | 674: 'mousetrap', 677 | 675: 'moving van', 678 | 676: 'muzzle', 679 | 677: 'nail', 680 | 678: 'neck brace', 681 | 679: 'necklace', 682 | 680: 'nipple', 683 | 681: 'notebook', 684 | 682: 'obelisk', 685 | 683: 'oboe', 686 | 684: 'ocarina', 687 | 685: 'odometer', 688 | 686: 'oil filter', 689 | 687: 'organ', 690 | 688: 'oscilloscope', 691 | 689: 'overskirt', 692 | 690: 'oxcart', 693 | 691: 'oxygen mask', 694 | 692: 'packet', 695 | 693: 'paddle', 696 | 694: 'paddlewheel', 697 | 695: 'padlock', 698 | 696: 'paintbrush', 699 | 697: 'pajama', 700 | 698: 'palace', 701 | 699: 'panpipe', 702 | 700: 'paper towel', 703 | 701: 'parachute', 704 | 702: 'parallel bars', 705 | 703: 'park bench', 706 | 704: 'parking meter', 707 | 705: 'passenger car', 708 | 706: 'patio', 709 | 707: 'pay-phone', 710 | 708: 'pedestal', 711 | 709: 'pencil box', 712 | 710: 'pencil sharpener', 713 | 711: 'perfume', 714 | 712: 'Petri dish', 715 | 713: 'photocopier', 716 | 714: 'pick', 717 | 715: 'pickelhaube', 718 | 716: 'picket fence', 719 | 717: 'pickup', 720 | 718: 'pier', 721 | 719: 'piggy bank', 722 | 720: 'pill bottle', 723 | 721: 'pillow', 724 | 722: 'ping-pong ball', 725 | 723: 'pinwheel', 726 | 724: 'pirate', 727 | 725: 'pitcher', 728 | 726: 'plane', 729 | 727: 'planetarium', 730 | 728: 'plastic bag', 731 | 729: 'plate rack', 732 | 730: 'plow', 733 | 731: 'plunger', 734 | 732: 'Polaroid camera', 735 | 733: 'pole', 736 | 734: 'police van', 737 | 735: 'poncho', 738 | 736: 'pool table', 739 | 737: 'pop bottle', 740 | 738: 'pot', 741 | 739: "potter's wheel", 742 | 740: 'power drill', 743 | 741: 'prayer rug', 744 | 742: 'printer', 745 | 743: 'prison', 746 | 744: 'projectile', 747 | 745: 'projector', 748 | 746: 'puck', 749 | 747: 'punching bag', 750 | 748: 'purse', 751 | 749: 'quill', 752 | 750: 'quilt', 753 | 751: 'racer', 754 | 752: 'racket', 755 | 753: 'radiator', 756 | 754: 'radio', 757 | 755: 'radio telescope', 758 | 756: 'rain barrel', 759 | 757: 'recreational vehicle', 760 | 758: 'reel', 761 | 759: 'reflex camera', 762 | 760: 'refrigerator', 763 | 761: 'remote control', 764 | 762: 'restaurant', 765 | 763: 'revolver', 766 | 764: 'rifle', 767 | 765: 'rocking chair', 768 | 766: 'rotisserie', 769 | 767: 'rubber eraser', 770 | 768: 'rugby ball', 771 | 769: 'rule', 772 | 770: 'running shoe', 773 | 771: 'safe', 774 | 772: 'safety pin', 775 | 773: 'saltshaker', 776 | 774: 'sandal', 777 | 775: 'sarong', 778 | 776: 'sax', 779 | 777: 'scabbard', 780 | 778: 'scale', 781 | 779: 'school bus', 782 | 780: 'schooner', 783 | 781: 'scoreboard', 784 | 782: 'screen', 785 | 783: 'screw', 786 | 784: 'screwdriver', 787 | 785: 'seat belt', 788 | 786: 'sewing machine', 789 | 787: 'shield', 790 | 788: 'shoe shop', 791 | 789: 'shoji', 792 | 790: 'shopping basket', 793 | 791: 'shopping cart', 794 | 792: 'shovel', 795 | 793: 'shower cap', 796 | 794: 'shower curtain', 797 | 795: 'ski', 798 | 796: 'ski mask', 799 | 797: 'sleeping bag', 800 | 798: 'slide rule', 801 | 799: 'sliding door', 802 | 800: 'slot', 803 | 801: 'snorkel', 804 | 802: 'snowmobile', 805 | 803: 'snowplow', 806 | 804: 'soap dispenser', 807 | 805: 'soccer ball', 808 | 806: 'sock', 809 | 807: 'solar dish', 810 | 808: 'sombrero', 811 | 809: 'soup bowl', 812 | 810: 'space bar', 813 | 811: 'space heater', 814 | 812: 'space shuttle', 815 | 813: 'spatula', 816 | 814: 'speedboat', 817 | 815: 'spider web', 818 | 816: 'spindle', 819 | 817: 'sports car', 820 | 818: 'spotlight', 821 | 819: 'stage', 822 | 820: 'steam locomotive', 823 | 821: 'steel arch bridge', 824 | 822: 'steel drum', 825 | 823: 'stethoscope', 826 | 824: 'stole', 827 | 825: 'stone wall', 828 | 826: 'stopwatch', 829 | 827: 'stove', 830 | 828: 'strainer', 831 | 829: 'streetcar', 832 | 830: 'stretcher', 833 | 831: 'studio couch', 834 | 832: 'stupa', 835 | 833: 'submarine', 836 | 834: 'suit', 837 | 835: 'sundial', 838 | 836: 'sunglass', 839 | 837: 'sunglasses', 840 | 838: 'sunscreen', 841 | 839: 'suspension bridge', 842 | 840: 'swab', 843 | 841: 'sweatshirt', 844 | 842: 'swimming trunks', 845 | 843: 'swing', 846 | 844: 'switch', 847 | 845: 'syringe', 848 | 846: 'table lamp', 849 | 847: 'tank', 850 | 848: 'tape player', 851 | 849: 'teapot', 852 | 850: 'teddy', 853 | 851: 'television', 854 | 852: 'tennis ball', 855 | 853: 'thatch', 856 | 854: 'theater curtain', 857 | 855: 'thimble', 858 | 856: 'thresher', 859 | 857: 'throne', 860 | 858: 'tile roof', 861 | 859: 'toaster', 862 | 860: 'tobacco shop', 863 | 861: 'toilet seat', 864 | 862: 'torch', 865 | 863: 'totem pole', 866 | 864: 'tow truck', 867 | 865: 'toyshop', 868 | 866: 'tractor', 869 | 867: 'trailer truck', 870 | 868: 'tray', 871 | 869: 'trench coat', 872 | 870: 'tricycle', 873 | 871: 'trimaran', 874 | 872: 'tripod', 875 | 873: 'triumphal arch', 876 | 874: 'trolleybus', 877 | 875: 'trombone', 878 | 876: 'tub', 879 | 877: 'turnstile', 880 | 878: 'typewriter keyboard', 881 | 879: 'umbrella', 882 | 880: 'unicycle', 883 | 881: 'upright', 884 | 882: 'vacuum', 885 | 883: 'vase', 886 | 884: 'vault', 887 | 885: 'velvet', 888 | 886: 'vending machine', 889 | 887: 'vestment', 890 | 888: 'viaduct', 891 | 889: 'violin', 892 | 890: 'volleyball', 893 | 891: 'waffle iron', 894 | 892: 'wall clock', 895 | 893: 'wallet', 896 | 894: 'wardrobe', 897 | 895: 'warplane', 898 | 896: 'washbasin', 899 | 897: 'washer', 900 | 898: 'water bottle', 901 | 899: 'water jug', 902 | 900: 'water tower', 903 | 901: 'whiskey jug', 904 | 902: 'whistle', 905 | 903: 'wig', 906 | 904: 'window screen', 907 | 905: 'window shade', 908 | 906: 'Windsor tie', 909 | 907: 'wine bottle', 910 | 908: 'wing', 911 | 909: 'wok', 912 | 910: 'wooden spoon', 913 | 911: 'wool', 914 | 912: 'worm fence', 915 | 913: 'wreck', 916 | 914: 'yawl', 917 | 915: 'yurt', 918 | 916: 'web site', 919 | 917: 'comic book', 920 | 918: 'crossword puzzle', 921 | 919: 'street sign', 922 | 920: 'traffic light', 923 | 921: 'book jacket', 924 | 922: 'menu', 925 | 923: 'plate', 926 | 924: 'guacamole', 927 | 925: 'consomme', 928 | 926: 'hot pot', 929 | 927: 'trifle', 930 | 928: 'ice cream', 931 | 929: 'ice lolly', 932 | 930: 'French loaf', 933 | 931: 'bagel', 934 | 932: 'pretzel', 935 | 933: 'cheeseburger', 936 | 934: 'hotdog', 937 | 935: 'mashed potato', 938 | 936: 'head cabbage', 939 | 937: 'broccoli', 940 | 938: 'cauliflower', 941 | 939: 'zucchini', 942 | 940: 'spaghetti squash', 943 | 941: 'acorn squash', 944 | 942: 'butternut squash', 945 | 943: 'cucumber', 946 | 944: 'artichoke', 947 | 945: 'bell pepper', 948 | 946: 'cardoon', 949 | 947: 'mushroom', 950 | 948: 'Granny Smith', 951 | 949: 'strawberry', 952 | 950: 'orange', 953 | 951: 'lemon', 954 | 952: 'fig', 955 | 953: 'pineapple', 956 | 954: 'banana', 957 | 955: 'jackfruit', 958 | 956: 'custard apple', 959 | 957: 'pomegranate', 960 | 958: 'hay', 961 | 959: 'carbonara', 962 | 960: 'chocolate sauce', 963 | 961: 'dough', 964 | 962: 'meat loaf', 965 | 963: 'pizza', 966 | 964: 'potpie', 967 | 965: 'burrito', 968 | 966: 'red wine', 969 | 967: 'espresso', 970 | 968: 'cup', 971 | 969: 'eggnog', 972 | 970: 'alp', 973 | 971: 'bubble', 974 | 972: 'cliff', 975 | 973: 'coral reef', 976 | 974: 'geyser', 977 | 975: 'lakeside', 978 | 976: 'promontory', 979 | 977: 'sandbar', 980 | 978: 'seashore', 981 | 979: 'valley', 982 | 980: 'volcano', 983 | 981: 'ballplayer', 984 | 982: 'groom', 985 | 983: 'scuba diver', 986 | 984: 'rapeseed', 987 | 985: 'daisy', 988 | 986: "yellow lady's slipper", 989 | 987: 'corn', 990 | 988: 'acorn', 991 | 989: 'hip', 992 | 990: 'buckeye', 993 | 991: 'coral fungus', 994 | 992: 'agaric', 995 | 993: 'gyromitra', 996 | 994: 'stinkhorn', 997 | 995: 'earthstar', 998 | 996: 'hen-of-the-woods', 999 | 997: 'bolete', 1000 | 998: 'ear', 1001 | 999: 'toilet tissue' 1002 | } -------------------------------------------------------------------------------- /src/model.py: -------------------------------------------------------------------------------- 1 | from typing import Dict 2 | 3 | import torch 4 | from torch import Tensor 5 | from torchvision import transforms 6 | from torchvision.models import inception_v3 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | 10 | from src.imagenet_class_labels import id2label 11 | 12 | 13 | def load_model() -> nn.Module: 14 | model = inception_v3(pretrained=True) 15 | model.eval() 16 | n_params = sum(p.numel() for p in model.parameters()) 17 | print(f'{n_params:,} parameters') # 27,161,264 parameters 18 | return model 19 | 20 | def predict(model: nn.Module, image) -> Dict: 21 | 22 | # resize and normalize input pixel ranges 23 | img = preprocess(image) 24 | 25 | output = model.forward(img) 26 | class_idx = torch.max(output.data, 1)[1][0].item() 27 | label = id2label[class_idx] 28 | output_probs = F.softmax(output, dim=1) 29 | confidence = round(torch.max(output_probs.data, 1)[0][0].item(), 4) 30 | 31 | return { 32 | 'id': class_idx, 33 | 'label': label, 34 | 'confidence': confidence, 35 | } 36 | 37 | 38 | def preprocess(img) -> Tensor: 39 | """ 40 | Inception V3 model from pytorch expects input images with pixel values between -1 and 1 41 | and dimensions 299 x 299 42 | """ 43 | mean = [0.485, 0.456, 0.406] 44 | std = [0.229, 0.224, 0.225] 45 | 46 | preprocess_fn = transforms.Compose([ 47 | transforms.Resize((299,299)), 48 | transforms.ToTensor(), 49 | transforms.Normalize(mean, std) 50 | ]) 51 | image_tensor = preprocess_fn(img) 52 | 53 | # add batch dimension: C x H x W ==> B x C x H x W 54 | image_tensor = image_tensor.unsqueeze(0) 55 | 56 | return image_tensor 57 | 58 | def inverse_preprocess(x: Tensor): 59 | """""" 60 | t = x.squeeze(0) 61 | 62 | mean = [0.485, 0.456, 0.406] 63 | std = [0.229, 0.224, 0.225] 64 | t = t.mul(torch.FloatTensor(std).view(3,1,1)).add(torch.FloatTensor(mean).view(3,1,1)) #.numpy() 65 | 66 | im = transforms.ToPILImage()(t) #.convert("RGB") #.Resize((299, 299)) 67 | 68 | return im 69 | -------------------------------------------------------------------------------- /src/streamlit_app.py: -------------------------------------------------------------------------------- 1 | import io 2 | 3 | import requests 4 | import streamlit as st 5 | # import pandas as pd 6 | # import numpy as np 7 | from PIL import Image 8 | 9 | from src.model import preprocess, inverse_preprocess, load_model, predict 10 | from src.fgsm import fast_gradient_sign, iterative_fast_gradient_sign_ 11 | 12 | st.title('Adversarial example generator') 13 | 14 | doc_markdown = """ 15 | ## What are adversarial examples? 💡 16 | 17 | 👉🏽 Do you think it is impossible to fool the vision system of a self-driving Tesla car? 18 | 19 | 👉🏽 Or that machine learning models used in malware detection software are too good to be evaded by hackers? 20 | 21 | 👉🏽 Or that face recognition systems in airports are bulletproof? 22 | 23 | Like any of us machine learning enthusiasts, you might fall into the trap of thinking that deep models used out there are perfect. 24 | 25 | ### Well, you are WRONG. 26 | 27 | There are easy ways to build **adversarial examples** that can fool any deep learning model and create security issues. 28 | 29 | With this app you can create your own adversarial examples, using the **Iterative Fast Gradient Sign Method**, and fool [`Inception-v3`](https://en.wikipedia.org/wiki/Inceptionv3) 30 | 31 | 32 | """ 33 | 34 | # The model 35 | @st.cache 36 | def load_model_(): 37 | return load_model() 38 | 39 | model = load_model_() 40 | 41 | st.sidebar.title('Iterative FGSM parameters') 42 | st.markdown(doc_markdown) 43 | 44 | # Input image 45 | example_url = 'https://github.com/Paulescu/adversarial-machine-learning/blob/main/images/dog.jpg?raw=true' 46 | # url = st.sidebar.text_input('Introduce URL of the initial image 👇🏼', example_url) 47 | st.markdown('## Original image') 48 | url = st.text_input('Introduce URL of the initial image 👇🏼', example_url) 49 | 50 | IMAGE_WIDTH = 350 51 | 52 | @st.cache 53 | def fetch_image(url): 54 | response = requests.get(url) 55 | x = Image.open(io.BytesIO(response.content)) 56 | x = inverse_preprocess(preprocess(x)) 57 | return x 58 | img = fetch_image(url) 59 | 60 | prediction = predict(model, img) 61 | caption = f'{prediction["label"]} \n {prediction["confidence"]:.0%}' 62 | st.image(img, caption=caption, width=IMAGE_WIDTH*2) 63 | 64 | # Selector parameters 65 | epsilon = st.sidebar.slider('Step size', min_value=0.0, max_value=0.25, step=0.01, value=0.09, format="%.3f") 66 | alpha = st.sidebar.slider('Max perturbation', min_value=0.00, max_value=0.250, step=0.001, value=0.025, format="%.3f") 67 | n_steps = st.sidebar.number_input('Number of steps', step=1, min_value=1, max_value=50, value=9) 68 | 69 | # image_width = 299 70 | st.markdown('## FGSM steps') 71 | image_width = int(IMAGE_WIDTH * 2 / 3) 72 | iterator = iterative_fast_gradient_sign_(model, preprocess(img), epsilon, n_steps=n_steps, alpha=alpha) 73 | counter = 1 74 | for x_adv, grad in iterator: 75 | 76 | st.markdown(f'## Step {counter}') 77 | 78 | # get model predictions 79 | prediction_adv = predict(model, x_adv) 80 | 81 | # print them 82 | caption_adv = f'= {prediction_adv["label"]} \n {prediction_adv["confidence"]:.0%}' 83 | st.image([x_adv, grad, x_adv], width=image_width, caption=['', f'* {epsilon}', caption_adv], output_format='JPEG') 84 | 85 | counter += 1 86 | 87 | # x_adv, grad = fast_gradient_sign(model, preprocess(img), epsilon, output_type='rgb') 88 | # prediction_adv = predict(model, x_adv) 89 | # for i in range(0, 3): 90 | # caption_adv = f'= {prediction_adv["label"]} \n {prediction_adv["confidence"]:.0%}' 91 | # st.image([grad, x_adv], width=image_width, caption=[f'* {epsilon}', caption_adv], output_format='JPEG') 92 | 93 | -------------------------------------------------------------------------------- /src/viz.py: -------------------------------------------------------------------------------- 1 | # from pdb import set_trace as stop 2 | 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | 6 | 7 | def plot(x, x_adv, x_grad, epsilon, x_label, x_prob, x_adv_label, x_adv_prob): 8 | """""" 9 | 10 | # figure, ax = plt.subplots(1,3, figsize=(18,8)) 11 | figure, ax = plt.subplots(1,3, figsize=(12,8)) 12 | ax[0].imshow(x) 13 | ax[0].set_title('Original', fontsize=16) 14 | 15 | ax[1].imshow(x_grad) 16 | ax[1].set_title('Perturbation', fontsize=16) 17 | ax[1].set_yticklabels([]) 18 | ax[1].set_xticklabels([]) 19 | ax[1].set_xticks([]) 20 | ax[1].set_yticks([]) 21 | 22 | ax[2].imshow(x_adv) 23 | ax[2].set_title('New', fontsize=16) 24 | 25 | ax[0].axis('off') 26 | ax[2].axis('off') 27 | 28 | ax[0].text(1.1,0.5, "+{}*".format(round(epsilon,3)), size=15, ha="center", 29 | transform=ax[0].transAxes) 30 | 31 | ax[0].text(0.5,-0.13, "Prediction: {}\n Probability: {:.0%}".format(x_label, x_prob), size=12, ha="center", 32 | transform=ax[0].transAxes) 33 | 34 | ax[1].text(1.1,0.5, " = ", size=15, ha="center", transform=ax[1].transAxes) 35 | 36 | ax[2].text(0.5,-0.13, "Prediction: {}\n Probability: {:.0%}".format(x_adv_label, x_adv_prob), size=12, ha="center", 37 | transform=ax[2].transAxes) 38 | 39 | 40 | plt.show() 41 | 42 | 43 | def plot_single(x): 44 | """""" 45 | # figure, ax = plt.subplots(1,3, figsize=(18,8)) 46 | figure, ax = plt.subplots(1,3, figsize=(12,8)) 47 | ax[0].imshow(x) 48 | ax[0].set_title('Clean Example', fontsize=16) 49 | 50 | plt.show() 51 | 52 | 53 | def visualize_OLD(x, x_adv, x_grad, epsilon): 54 | """""" 55 | # get predicted label and confidence of x 56 | x_predictions = get_predictions(x) 57 | x_label = x_predictions.label 58 | x_prob = x_predictions.confidence 59 | 60 | # get predicted label and confidence of x_adv 61 | x_adv_predictions = get_predictions(x_adv) 62 | x_adv_label = x_adv_predictions.label 63 | x_adv_prob = x_adv_predictions.confidence 64 | 65 | # transform x into its raw form for visualization 66 | x = x.squeeze(0) # remove batch dimension: B x C x H x W ==> C x H x W 67 | x = inverse_preprocess(x) 68 | x = np.transpose( x , (1,2,0)) # C x H x W ==> H x W x C 69 | x = np.clip(x, 0, 1) 70 | 71 | # transform x_adv into its raw form for visualization 72 | x_adv = x_adv.squeeze(0) 73 | x_adv = inverse_preprocess(x_adv) 74 | x_adv = np.transpose( x_adv , (1,2,0)) # C X H X W ==> H X W X C 75 | x_adv = np.clip(x_adv, 0, 1) 76 | 77 | # transform grad into its raw form for visualization 78 | x_grad = x_grad.squeeze(0).numpy() 79 | x_grad = np.transpose(x_grad, (1,2,0)) 80 | x_grad = np.clip(x_grad, 0, 1) 81 | 82 | # ---------------- 83 | # start plotting 84 | # ---------------- 85 | figure, ax = plt.subplots(1,3, figsize=(18,8)) 86 | ax[0].imshow(x) 87 | ax[0].set_title('Clean Example', fontsize=20) 88 | 89 | ax[1].imshow(x_grad) 90 | ax[1].set_title('Perturbation', fontsize=20) 91 | ax[1].set_yticklabels([]) 92 | ax[1].set_xticklabels([]) 93 | ax[1].set_xticks([]) 94 | ax[1].set_yticks([]) 95 | 96 | 97 | ax[2].imshow(x_adv) 98 | ax[2].set_title('Adversarial Example', fontsize=20) 99 | 100 | ax[0].axis('off') 101 | ax[2].axis('off') 102 | 103 | ax[0].text(1.1,0.5, "+{}*".format(round(epsilon,3)), size=15, ha="center", 104 | transform=ax[0].transAxes) 105 | 106 | ax[0].text(0.5,-0.13, "Prediction: {}\n Probability: {}".format(x_label, x_prob), size=15, ha="center", 107 | transform=ax[0].transAxes) 108 | 109 | ax[1].text(1.1,0.5, " = ", size=15, ha="center", transform=ax[1].transAxes) 110 | 111 | ax[2].text(0.5,-0.13, "Prediction: {}\n Probability: {}".format(x_adv_label, x_adv_prob), size=15, ha="center", 112 | transform=ax[2].transAxes) 113 | 114 | 115 | plt.show() --------------------------------------------------------------------------------