SecML
v0.10

User Guide

  • 1. Training of Classifiers and Visualization of Results
  • 2. Evasion Attacks against Machine Learning models
  • 3. Transferability of Evasion Attacks
  • 4. Poisoning Attacks against Machine Learning models
  • 5. Evasion and Poisoning Attacks on MNIST dataset
  • 6. Neural Networks with PyTorch
  • 7. Evasion Attacks against Neural Networks on MNIST dataset
  • 8. Evasion Attacks on ImageNet dataset
  • 9. Explaining Machine Learning

Core & Data Structures

  • secml.core
    • CCreator
    • attr_utils
    • constants
    • decorators
    • exceptions
    • type_utils
  • secml.array
    • CArray
    • array_utils
  • secml.data
    • secml.data.loader
      • CDataLoader
      • CDataLoaderCIFAR
      • CDataLoaderICubWorld
      • CDataLoaderImgClients
      • CDataLoaderImgFolders
      • CDataLoaderLFW
      • CDataLoaderMNIST
      • CDataLoaderPyTorch
      • CDataLoaderSkLearn
      • CDataLoaderSvmLight
      • CDataLoaderTorchDataset
      • loader_utils
    • secml.data.selection
      • CPrototypesSelector
      • CPSBorder
      • CPSCenter
      • CPSKMedians
      • CPSRandom
      • CPSSpanning
    • secml.data.splitter
      • CDataSplitter
      • CDataSplitterKFold
      • CDataSplitterLabelKFold
      • CDataSplitterOpenWorldKFold
      • CDataSplitterShuffle
      • CDataSplitterStratifiedKFold
      • CTrainTestSplit
      • CChronologicalSplitter
    • CDataset
    • CDatasetHeader
    • CDatasetPyTorch
    • data_utils

Machine Learning

  • secml.ml
    • secml.ml.classifiers
      • secml.ml.classifiers.multiclass
        • CClassifierMulticlass
        • CClassifierMulticlassOVA
      • secml.ml.classifiers.reject
        • CClassifierReject
        • CClassifierRejectThreshold
      • secml.ml.classifiers.loss
        • CLoss
        • CLossCrossEntropy
        • CLossEpsilonInsensitive
        • CLossHinge
        • CLossLogistic
        • CLossSquare
        • CSoftmax
      • secml.ml.classifiers.regularizer
        • CRegularizer
        • CRegularizerElasticNet
        • CRegularizerL1
        • CRegularizerL2
      • CClassifier
      • CClassifierLinear
      • CClassifierSkLearn
      • CClassifierDecisionTree
      • CClassifierKNN
      • CClassifierLogistic
      • CClassifierNearestCentroid
      • CClassifierRandomForest
      • CClassifierRidge
      • CClassifierSGD
      • CClassifierSVM
      • CClassifierPyTorch
      • CModelCleverhans
      • clf_utils
    • secml.ml.features
      • secml.ml.features.normalization
        • CNormalizer
        • CNormalizerLinear
        • CNormalizerMeanStd
        • CNormalizerMinMax
        • CNormalizerUnitNorm
        • CNormalizerDNN
      • secml.ml.features.reduction
        • CReducer
        • CLDA
        • CPCA
      • CPreProcess
    • secml.ml.kernel
      • CKernel
      • CKernelChebyshevDistance
      • CKernelEuclidean
      • CKernelHistIntersect
      • CKernelLaplacian
      • CKernelLinear
      • CKernelPoly
      • CKernelRBF
    • secml.ml.peval
      • secml.ml.peval.metrics
        • CMetric
        • CMetricAccuracy
        • CMetricAUC
        • CMetricAUCWMW
        • CMetricConfusionMatrix
        • CMetricF1
        • CMetricMAE
        • CMetricMSE
        • CMetricPartialAUC
        • CMetricPrecision
        • CMetricRecall
        • CRoc
        • CMetricTestError
        • CMetricTPRatFPR
      • CPerfEvaluator
      • CPerfEvaluatorXVal
      • CPerfEvaluatorXValMulticlass
    • secml.ml.stats
      • CDensityEstimation
      • CDistributionGaussian
  • secml.adv
    • secml.adv.attacks
      • secml.adv.attacks.evasion
        • CAttackEvasion
        • CAttackEvasionPGD
        • CAttackEvasionPGDLS
        • CAttackEvasionCleverhans
      • secml.adv.attacks.poisoning
        • CAttackPoisoning
        • CAttackPoisoningLogisticRegression
        • CAttackPoisoningRidge
        • CAttackPoisoningSVM
      • CAttack
    • secml.adv.seceval
      • CSecEval
      • CSecEvalData
  • secml.optim
    • secml.optim.function
      • CFunction
      • CFunctionLinear
      • CFunctionQuadratic
      • CFunctionRosenbrock
      • CFunctionThreeHumpCamel
      • CFunctionBeale
      • CFunctionMcCormick
    • secml.optim.optimizers
      • secml.optim.optimizers.line_search
        • CLineSearch
        • CLineSearchBisect
      • COptimizer
      • COptimizerPGDLS
      • COptimizerPGD
      • COptimizerScipy
    • secml.optim.constraints
      • CConstraint
      • CConstraintBox
      • CConstraintL1
      • CConstraintL2

Explanation

  • secml.explanation
    • CExplainer
    • CExplainerGradient
    • CExplainerGradientInput
    • CExplainerIntegratedGradients
    • CExplainerInfluenceFunctions

Visualization

  • secml.figure
    • CFigure
    • CPlot

Utilities

  • secml.parallel
    • parfor
  • secml.utils
    • CLog
    • c_file_manager
    • pickle_utils
    • download_utils
    • dict_utils
    • list_utils
    • mixed_utils
  • secml.settings
  • secml.testing
    • CUnitTest

References

  • UPDATE GUIDES
    • From 0.8.* to 0.9
      • 1. Configuration file
      • 2. Deprecations
  • CHANGELOG
    • v0.10 (29/10/2019)
      • Requirements (1 change)
      • Added (4 changes)
      • Improved (6 changes)
      • Changed (4 changes)
      • Fixed (4 changes)
      • Removed & Deprecated (5 changes)
      • Documentation (5 changes)
    • v0.9 (11/10/2019)
      • Improved (1 change)
      • Changed (1 change)
      • Fixed (1 change)
      • Documentation (2 changes)
      • Deprecations (3 changes)
    • v0.8.1 (05/09/2019)
      • Documentation (2 changes)
    • v0.8 (06/08/2019)
  • ROADMAP
SecML
  • Docs »
  • secml.adv
  • Edit on GitLab

secml.advΒΆ

Adversarial Machine Learning

  • secml.adv.attacks
    • secml.adv.attacks.evasion
      • CAttackEvasion
      • CAttackEvasionPGD
      • CAttackEvasionPGDLS
      • CAttackEvasionCleverhans
    • secml.adv.attacks.poisoning
      • CAttackPoisoning
      • CAttackPoisoningLogisticRegression
      • CAttackPoisoningRidge
      • CAttackPoisoningSVM
    • CAttack
  • secml.adv.seceval
    • CSecEval
    • CSecEvalData
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© Copyright 2019, PRALab - Pattern Recognition and Applications Lab & Pluribus One s.r.l. Revision f99cf9ba.

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