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توضیحات

Adaptative Perturbation Pattern Method

ویژگی	مقدار
سیستم عامل	-
نام فایل	a2pm-1.2.0
نام	a2pm
نسخه کتابخانه	1.2.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	João Vitorino
ایمیل نویسنده	jpmvo@outlook.com
آدرس صفحه اصلی	https://github.com/vitorinojoao/a2pm
آدرس اینترنتی	https://pypi.org/project/a2pm/
مجوز	MIT

Adaptative Perturbation Pattern Method ====================================== A2PM is a gray-box method for the generation of realistic adversarial examples. It benefits from a modular architecture to assign an independent sequence of adaptative perturbation patterns to each class, which analyze specific feature subsets to create valid and coherent data perturbations. This method was developed to address the diverse constraints of domains with tabular data, such as cybersecurity. It can be advantageous for adversarial attacks against machine learning classifiers, as well as for adversarial training strategies. This Python 3 implementation provides out-of-the-box compatibility with the Scikit-Learn library. If you use A2PM, please cite the primary research article: `https://doi.org/10.3390/fi14040108 <https://doi.org/10.3390/fi14040108>`_ Check out the official documentation: `https://a2pm.readthedocs.io/en/latest <https://a2pm.readthedocs.io/en/latest/>`_ Explore the public source code repository: `https://github.com/vitorinojoao/a2pm <https://github.com/vitorinojoao/a2pm>`_ .. figure:: https://raw.githubusercontent.com/vitorinojoao/a2pm/main/images/a2pm.png :alt: A2PMFigure How To Install -------------- The package and its dependencies can be installed using the pip package manager: :: pip install a2pm Alternatively, the repository can be downloaded and the package installed from the local directory: :: pip install . How To Setup ------------ The package can be accessed through the following imports: .. code:: python from a2pm import A2PMethod from a2pm.callbacks import BaseCallback, MetricCallback, TimeCallback from a2pm.patterns import BasePattern, CombinationPattern, IntervalPattern from a2pm.wrappers import BaseWrapper, KerasWrapper, SklearnWrapper, TorchWrapper A2PM can be created with a simple base configuration of Interval and/or Combination pattern sequences, which have several possible parameters: .. code:: python pattern = ( # First pattern to be applied: Interval { "type": "interval", "features": list(range(0, 20)), "integer_features": list(range(10, 20)), "ratio": 0.1, "max_ratio": 0.3, "missing_value": 0.0, "probability": 0.6, }, # Second pattern to be applied: Combination { "type": "combination", "features": list(range(20, 40)), "locked_features": list(range(30, 40)), "probability": 0.4, }, ) method = A2PMethod(pattern) To support domains with complex constraints, the method is highly configurable: .. code:: python # General pattern sequence that will be applied to new data classes pattern = ( # An instantiated pattern MyCustomPattern(1, 2), # A pattern configuration { "type": MyCustomPattern, "param_name_1": 3, "param_name_2": 4, }, ) # Pre-assigned mapping of data classes to pattern sequences preassigned_patterns = { # None to disable the perturbation of this class "class_label_1": None, # Specific pattern sequence that will be applied to this class "class_label_2": ( MyCustomPattern(5, 6), { "type": MyCustomPattern, "param_name_1": 7, "param_name_2": 8, }, ), } method = A2PMethod(pattern, preassigned_patterns) How To Use ---------- A2PM can be utilized through the 'fit', 'partial_fit', 'transform' and 'generate' methods, as well as their respective shortcuts: .. code:: python # Adapts to new data, and then creates adversarial examples X_adversarial = method.fit_transform(X, y) # Encapsulates a Tensorflow/Keras classification model classifier = KerasWrapper(my_model, my_custom_class_labels) # Adapts to new data, and then performs an untargeted attack against a classifier X_adversarial = method.fit_generate(classifier, X, y) # Adapts to new data, and then performs a targeted attack against a classifier X_adversarial = method.fit_generate(classifier, X, y, y_target) To analyze specific aspects of the method, callback functions can be called before the attack starts (iteration 0) and after each attack iteration (iteration 1, 2, ...): .. code:: python X_adversarial = method.fit_generate( classifier, X, y, y_target, # Additional configuration iterations=10, patience=2, callback=[ # Time consumption TimeCallback(verbose=2), # Evaluation metrics MetricCallback(classifier, y, my_custom_scorers, verbose=2), # An instantiated callback MyCustomCallback(), # A simple callback function MyCustomFunction, ], )

نیازمندی

مقدار	نام
<2,>=1.17.5	numpy
<2,>=0.23.2	scikit-learn

زبان مورد نیاز

مقدار	نام
>=3.5	Python

نحوه نصب

نصب پکیج whl a2pm-1.2.0:

pip install a2pm-1.2.0.whl

نصب پکیج tar.gz a2pm-1.2.0:

pip install a2pm-1.2.0.tar.gz