art.estimators.certification.randomized_smoothing
¶
Randomized smoothing estimators.
Mixin Base Class Randomized Smoothing¶
- class art.estimators.certification.randomized_smoothing.RandomizedSmoothingMixin(sample_size: int, *args, scale: float = 0.1, alpha: float = 0.001, **kwargs)¶
Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced in Cohen et al. (2019).
Paper link: https://arxiv.org/abs/1902.02918- certify(x: numpy.ndarray, n: int, batch_size: int = 32) Tuple[numpy.ndarray, numpy.ndarray] ¶
Computes certifiable radius around input x and returns radius r and prediction.
- Return type
Tuple
- Parameters
x (
ndarray
) – Sample input with shape as expected by the model.n (
int
) – Number of samples for estimate certifiable radius.batch_size (
int
) – Batch size.
- Returns
Tuple of length 2 of the selected class and certified radius.
- fit(x: numpy.ndarray, y: numpy.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) None ¶
Fit the classifier on the training set (x, y).
- Parameters
x (
ndarray
) – Training data.y (
ndarray
) – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).batch_size (
int
) – Batch size.nb_epochs (
int
) – Number of epochs to use for training.kwargs – Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch and providing it takes no effect.
- predict(x: numpy.ndarray, batch_size: int = 128, **kwargs) numpy.ndarray ¶
Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
- Return type
ndarray
- Parameters
x (
ndarray
) – Input samples.batch_size (
int
) – Batch size.is_abstain (boolean) – True if function will abstain from prediction and return 0s. Default: True
- Returns
Array of predictions of shape (nb_inputs, nb_classes).
PyTorch Randomized Smoothing Classifier¶
- class art.estimators.certification.randomized_smoothing.PyTorchRandomizedSmoothing(model: torch.nn.Module, loss: torch.nn.modules.loss._Loss, input_shape: Tuple[int, ...], nb_classes: int, optimizer: Optional[torch.optim.Optimizer] = None, channels_first: bool = True, clip_values: Optional[CLIP_VALUES_TYPE] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0), device_type: str = 'gpu', sample_size: int = 32, scale: float = 0.1, alpha: float = 0.001)¶
Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced in Cohen et al. (2019).
Paper link: https://arxiv.org/abs/1902.02918- __init__(model: torch.nn.Module, loss: torch.nn.modules.loss._Loss, input_shape: Tuple[int, ...], nb_classes: int, optimizer: Optional[torch.optim.Optimizer] = None, channels_first: bool = True, clip_values: Optional[CLIP_VALUES_TYPE] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0), device_type: str = 'gpu', sample_size: int = 32, scale: float = 0.1, alpha: float = 0.001)¶
Create a randomized smoothing classifier.
- Parameters
model – PyTorch model. The output of the model can be logits, probabilities or anything else. Logits output should be preferred where possible to ensure attack efficiency.
loss – The loss function for which to compute gradients for training. The target label must be raw categorical, i.e. not converted to one-hot encoding.
input_shape (
Tuple
) – The shape of one input instance.nb_classes (
int
) – The number of classes of the model.optimizer – The optimizer used to train the classifier.
channels_first (
bool
) – Set channels first or last.clip_values – Tuple of the form (min, max) of floats or np.ndarray representing the minimum and maximum values allowed for features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus the shape of clip values needs to match the total number of features.
preprocessing_defences – Preprocessing defence(s) to be applied by the classifier.
postprocessing_defences – Postprocessing defence(s) to be applied by the classifier.
preprocessing – Tuple of the form (subtrahend, divisor) of floats or np.ndarray of values to be used for data preprocessing. The first value will be subtracted from the input. The input will then be divided by the second one.
device_type (
str
) – Type of device on which the classifier is run, either gpu or cpu.sample_size (
int
) – Number of samples for smoothing.scale (
float
) – Standard deviation of Gaussian noise added.alpha (
float
) – The failure probability of smoothing.
- certify(x: numpy.ndarray, n: int, batch_size: int = 32) Tuple[numpy.ndarray, numpy.ndarray] ¶
Computes certifiable radius around input x and returns radius r and prediction.
- Return type
Tuple
- Parameters
x (
ndarray
) – Sample input with shape as expected by the model.n (
int
) – Number of samples for estimate certifiable radius.batch_size (
int
) – Batch size.
- Returns
Tuple of length 2 of the selected class and certified radius.
- 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 of the given classifier w.r.t. x of original classifier.
- 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 clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns
Clip values (min, max).
- clone_for_refitting() art.estimators.classification.pytorch.PyTorchClassifier ¶
Create a copy of the classifier that can be refit from scratch. Will inherit same architecture, optimizer and initialization as cloned model, but without weights.
- Returns
new estimator
- compute_loss(x: Union[numpy.ndarray, torch.Tensor], y: Union[numpy.ndarray, torch.Tensor], reduction: str = 'none', **kwargs) Union[numpy.ndarray, torch.Tensor] ¶
Compute the loss.
- Parameters
x – Sample input with shape as expected by the model.
y – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
reduction (
str
) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed.
- Returns
Array of losses of the same shape as x.
- compute_losses(x: Union[numpy.ndarray, torch.Tensor], y: Union[numpy.ndarray, torch.Tensor], reduction: str = 'none') Dict[str, Union[numpy.ndarray, torch.Tensor]] ¶
Compute all loss components.
- Return type
Dict
- Parameters
x – Sample input with shape as expected by the model.
y – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
reduction (
str
) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed.
- Returns
Dictionary of loss components.
- property device: torch.device¶
Get current used device.
- Returns
Current used device.
- property device_type: str¶
Return the type of device on which the estimator is run.
- Returns
Type of device on which the estimator is run, either gpu or cpu.
- fit(*args, **kwargs)¶
Fit the classifier on the training set (x, y).
- Parameters
x – Training data.
y – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
batch_size – Batch size.
kwargs (dict) – Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch and providing it takes no effect.
- Key nb_epochs
Number of epochs to use for training
- Returns
None
- fit_generator(generator: DataGenerator, nb_epochs: int = 20, **kwargs) None ¶
Fit the classifier using the generator that yields batches as specified.
- Parameters
generator – Batch generator providing (x, y) for each epoch.
nb_epochs (
int
) – Number of epochs to use for training.kwargs – Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch and providing it takes no effect.
- get_activations(x: Union[numpy.ndarray, torch.Tensor], layer: Optional[Union[str, int]] = None, batch_size: int = 128, 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.
- Return type
ndarray
- Parameters
x – Input for computing the activations.
layer – Layer for computing the activations
batch_size (
int
) – Size of batches.framework (
bool
) – If true, return the intermediate tensor representation of the activation.
- Returns
The output of layer, where the first dimension is the batch size corresponding to x.
- get_params() Dict[str, Any] ¶
Get all parameters and their values of this estimator.
- Returns
A dictionary of string parameter names to their value.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- property layer_names: Optional[List[str]]¶
Return the names of the hidden layers in the model, if applicable.
- Returns
The names of the hidden layers in the model, input and output layers are ignored.
Warning
layer_names tries to infer the internal structure of the model. This feature comes with no guarantees on the correctness of the result. The intended order of the layers tries to match their order in the model, but this is not guaranteed either.
- property loss: torch.nn.modules.loss._Loss¶
Return the loss function.
- Returns
The loss function.
- 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.sampling (bool) – True if loss gradients should be determined with Monte Carlo sampling.
- Returns
Array of gradients of the same shape as x.
- property loss_scale: Union[float, str]¶
Return the loss scaling value.
- Returns
Loss scaling. Possible values for string: a string representing a number, e.g., “1.0”, or the string “dynamic”.
- property model: torch.nn.Module¶
Return the model.
- Returns
The model.
- property nb_classes: int¶
Return the number of output classes.
- Returns
Number of classes in the data.
- property opt_level: str¶
Return a string specifying a pure or mixed precision optimization level.
- Returns
A string specifying a pure or mixed precision optimization level. Possible values are O0, O1, O2, and O3.
- property optimizer: torch.optim.Optimizer¶
Return the optimizer.
- Returns
The optimizer.
- predict(*args, **kwargs)¶
Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
- Parameters
x – Input samples.
batch_size – Batch size.
is_abstain (boolean) – True if function will abstain from prediction and return 0s. Default: True
- Returns
Array of predictions of shape (nb_inputs, nb_classes).
- reduce_labels(y: Union[numpy.ndarray, torch.Tensor]) Union[numpy.ndarray, torch.Tensor] ¶
Reduce labels from one-hot encoded to index labels.
- reset() None ¶
Resets the weights of the classifier so that it can be refit from scratch.
- save(filename: str, path: Optional[str] = None) None ¶
Save a model to file in the format specific to the backend framework.
- 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.
- set_batchnorm(train: bool) None ¶
Set all batch normalization layers into train or eval mode.
- Parameters
train (
bool
) – False for evaluation mode.
- set_dropout(train: bool) None ¶
Set all dropout layers into train or eval mode.
- Parameters
train (
bool
) – False for evaluation mode.
- set_params(**kwargs) None ¶
Take a dictionary of parameters and apply checks before setting them as attributes.
- Parameters
kwargs – A dictionary of attributes.
- property use_amp: bool¶
Return a boolean indicating whether to use the automatic mixed precision tool.
- Returns
Whether to use the automatic mixed precision tool.
TensorFlow V2 Randomized Smoothing Classifier¶
- class art.estimators.certification.randomized_smoothing.TensorFlowV2RandomizedSmoothing(model, nb_classes: int, input_shape: Tuple[int, ...], loss_object: Optional[tf.Tensor] = None, train_step: Optional[Callable] = None, channels_first: bool = False, clip_values: Optional[CLIP_VALUES_TYPE] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0), sample_size: int = 32, scale: float = 0.1, alpha: float = 0.001)¶
Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced in Cohen et al. (2019).
Paper link: https://arxiv.org/abs/1902.02918- __init__(model, nb_classes: int, input_shape: Tuple[int, ...], loss_object: Optional[tf.Tensor] = None, train_step: Optional[Callable] = None, channels_first: bool = False, clip_values: Optional[CLIP_VALUES_TYPE] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0), sample_size: int = 32, scale: float = 0.1, alpha: float = 0.001)¶
Create a randomized smoothing classifier.
- Parameters
model (function or callable class) – a python functions or callable class defining the model and providing it prediction as output.
nb_classes (
int
) – the number of classes in the classification task.input_shape (
Tuple
) – Shape of one input for the classifier, e.g. for MNIST input_shape=(28, 28, 1).loss_object – The loss function for which to compute gradients. This parameter is applied for training the model and computing gradients of the loss w.r.t. the input.
train_step – A function that applies a gradient update to the trainable variables.
channels_first (
bool
) – Set channels first or last.clip_values – Tuple of the form (min, max) of floats or np.ndarray representing the minimum and maximum values allowed for features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus the shape of clip values needs to match the total number of features.
preprocessing_defences – Preprocessing defence(s) to be applied by the classifier.
postprocessing_defences – Postprocessing defence(s) to be applied by the classifier.
preprocessing – Tuple of the form (subtrahend, divisor) of floats or np.ndarray of values to be used for data preprocessing. The first value will be subtracted from the input. The input will then be divided by the second one.
sample_size (
int
) – Number of samples for smoothing.scale (
float
) – Standard deviation of Gaussian noise added.alpha (
float
) – The failure probability of smoothing.
- certify(x: numpy.ndarray, n: int, batch_size: int = 32) Tuple[numpy.ndarray, numpy.ndarray] ¶
Computes certifiable radius around input x and returns radius r and prediction.
- Return type
Tuple
- Parameters
x (
ndarray
) – Sample input with shape as expected by the model.n (
int
) – Number of samples for estimate certifiable radius.batch_size (
int
) – Batch size.
- Returns
Tuple of length 2 of the selected class and certified radius.
- 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 of the given classifier w.r.t. x of original classifier.
- 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 clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns
Clip values (min, max).
- clone_for_refitting() art.estimators.classification.tensorflow.TensorFlowV2Classifier ¶
Create a copy of the classifier that can be refit from scratch. Will inherit same architecture, optimizer and initialization as cloned model, but without weights.
- Returns
new estimator
- compute_loss(x: numpy.ndarray, y: numpy.ndarray, reduction: str = 'none', training_mode: bool = False, **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,).reduction (
str
) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed.training_mode (
bool
) – True for model set to training mode and ‘False for model set to evaluation mode.
- Returns
Loss values.
- Return type
Format as expected by the model
- compute_losses(x: Union[numpy.ndarray, tf.Tensor], y: Union[numpy.ndarray, tf.Tensor], reduction: str = 'none') Dict[str, Union[numpy.ndarray, tf.Tensor]] ¶
Compute all loss components.
- Return type
Dict
- Parameters
x – Sample input with shape as expected by the model.
y – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
reduction (
str
) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed.
- Returns
Dictionary of loss components.
- fit(*args, **kwargs)¶
Fit the classifier on the training set (x, y).
- Parameters
x – Training data.
y – Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape (nb_samples,).
batch_size – Batch size.
kwargs (dict) – Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch and providing it takes no effect.
- Key nb_epochs
Number of epochs to use for training
- Returns
None
- fit_generator(generator: DataGenerator, nb_epochs: int = 20, **kwargs) None ¶
Fit the classifier using the generator that yields batches as specified.
- Parameters
generator – Batch generator providing (x, y) for each epoch. If the generator can be used for native training in TensorFlow, it will.
nb_epochs (
int
) – Number of epochs to use for training.kwargs – Dictionary of framework-specific arguments. This parameter is not currently supported for TensorFlow and providing it takes no effect.
- get_activations(x: numpy.ndarray, layer: Union[int, str], batch_size: int = 128, framework: bool = False) Optional[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.
- Parameters
x (
ndarray
) – Input for computing the activations.layer – Layer for computing the activations.
batch_size (
int
) – Batch size.
- Returns
The output of layer, where the first dimension is the batch size corresponding to x.
- get_params() Dict[str, Any] ¶
Get all parameters and their values of this estimator.
- Returns
A dictionary of string parameter names to their value.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- property layer_names: Optional[List[str]]¶
Return the hidden layers in the model, if applicable.
- Returns
The hidden layers in the model, input and output layers excluded.
Warning
layer_names tries to infer the internal structure of the model. This feature comes with no guarantees on the correctness of the result. The intended order of the layers tries to match their order in the model, but this is not guaranteed either.
- 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
) – Correct labels, one-vs-rest encoding.training_mode (
bool
) – True for model set to training mode and ‘False for model set to evaluation mode.sampling (bool) – True if loss gradients should be determined with Monte Carlo sampling.
- Returns
Array of gradients of the same shape as x.
- property loss_object: tf.keras.losses.Loss¶
Return the loss function.
- Returns
The loss function.
- property model¶
Return the model.
- Returns
The model.
- property nb_classes: int¶
Return the number of output classes.
- Returns
Number of classes in the data.
- predict(*args, **kwargs)¶
Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
- Parameters
x – Input samples.
batch_size – Batch size.
is_abstain (boolean) – True if function will abstain from prediction and return 0s. Default: True
- Returns
Array of predictions of shape (nb_inputs, nb_classes).
- reset() None ¶
Resets the weights of the classifier so that it can be refit from scratch.
- save(filename: str, path: Optional[str] = None) None ¶
Save a model to file in the format specific to the backend framework. For TensorFlow, .ckpt is used.
- 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.
- set_params(**kwargs) None ¶
Take a dictionary of parameters and apply checks before setting them as attributes.
- Parameters
kwargs – A dictionary of attributes.
- property train_step: Callable¶
Return the function that applies a gradient update to the trainable variables.
- Returns
The function that applies a gradient update to the trainable variables.