art.defences.detector.evasion.subsetscanning¶
This module implements the fast generalized subset scan based detector.
Subset Scanning Detector¶
- class art.defences.detector.evasion.subsetscanning.SubsetScanningDetector(classifier: art.estimators.classification.classifier.ClassifierNeuralNetwork, bgd_data: numpy.ndarray, layer: Union[int, str], verbose: bool = True)¶
Fast generalized subset scan based detector by McFowland, E., Speakman, S., and Neill, D. B. (2013).
- calculate_pvalue_ranges(eval_x: numpy.ndarray) numpy.ndarray¶
Returns computed p-value ranges.
- Return type
ndarray- Parameters
eval_x (
ndarray) – Data being evaluated for anomalies.- Returns
P-value ranges.
- property channels_first: bool¶
- Returns
Boolean to indicate index of the color channels in the sample x.
- class_gradient(x: numpy.ndarray, label: Optional[Union[int, List[int]]] = None, training_mode: bool = False, **kwargs) numpy.ndarray¶
Compute per-class derivatives w.r.t. x.
- Return type
ndarray- Parameters
x (
ndarray) – Sample input with shape as expected by the model.label – Index of a specific per-class derivative. If an integer is provided, the gradient of that class output is computed for all samples. If multiple values as provided, the first dimension should match the batch size of x, and each value will be used as target for its corresponding sample in x. If None, then gradients for all classes will be computed for each sample.
training_mode (
bool) – True for model set to training mode and ‘False for model set to evaluation mode.
- Returns
Array of gradients of input features w.r.t. each class in the form (batch_size, nb_classes, input_shape) when computing for all classes, otherwise shape becomes (batch_size, 1, input_shape) when label parameter is specified.
- property classifier: art.estimators.classification.classifier.ClassifierNeuralNetwork¶
- Returns
Classifier.
- property clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns
Clip values (min, max).
- compute_loss(x: numpy.ndarray, y: numpy.ndarray, **kwargs) numpy.ndarray¶
Compute the loss of the neural network for samples x.
- Parameters
x (
ndarray) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (
ndarray) – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
- Returns
Loss values.
- Return type
Format as expected by the model
- fit(*args, **kwargs)¶
Fit the detector using training data. Assumes that the classifier is already trained.
- Raises
NotImplementedException – This method is not supported for detectors.
- fit_generator(generator: DataGenerator, nb_epochs: int = 20, **kwargs) None¶
Fit the classifier using the generator gen that yields batches as specified. This function is not supported for this detector.
- Raises
NotImplementedException – This method is not supported for detectors.
- get_activations(x: numpy.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False) numpy.ndarray¶
Return the output of the specified layer for input x. layer is specified by layer index (between 0 and nb_layers - 1) or by name. The number of layers can be determined by counting the results returned by calling layer_names. This function is not supported for this detector.
- Raises
NotImplementedException – This method is not supported for detectors.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- loss_gradient(x: numpy.ndarray, y: numpy.ndarray, training_mode: bool = False, **kwargs) numpy.ndarray¶
Compute the gradient of the loss function w.r.t. x.
- Return type
ndarray- Parameters
x (
ndarray) – Sample input with shape as expected by the model.y (
ndarray) – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).training_mode (
bool) – True for model set to training mode and ‘False for model set to evaluation mode.
- Returns
Array of gradients of the same shape as x.
- predict(*args, **kwargs)¶
Perform detection of adversarial data and return prediction as tuple.
- Raises
NotImplementedException – This method is not supported for detectors.
- save(filename: str, path: Optional[str] = None) None¶
Save a model to file in the format specific to the backend framework. This function is not supported for ensembles.
- Parameters
filename (
str) – Name of the file where to store the model.path – Path of the folder where to store the model. If no path is specified, the model will be stored in the default data location of the library ART_DATA_PATH.
- scan(clean_x: numpy.ndarray, adv_x: numpy.ndarray, clean_size: Optional[int] = None, advs_size: Optional[int] = None, run: int = 10) Tuple[list, list, float]¶
Returns scores of highest scoring subsets.
- Return type
Tuple- Parameters
clean_x (
ndarray) – Data presumably without anomalies.adv_x (
ndarray) – Data presumably with anomalies (adversarial samples).clean_size –
advs_size –
run (
int) –
- Returns
(clean_scores, adv_scores, detectionpower).