mindarmour.privacy.evaluation
This module provides some methods to evaluate the risk of privacy leakage of given model.
- class mindarmour.privacy.evaluation.ImageInversionAttack(network, input_shape, input_bound, loss_weights=(1, 0.2, 5))[source]
An attack method used to reconstruct images by inverting their deep representations.
References: Aravindh Mahendran, Andrea Vedaldi. Understanding Deep Image Representations by Inverting Them. 2014.
- Parameters
network (Cell) – The network used to infer images’ deep representations.
input_shape (tuple) – Data shape of single network input, which should be in accordance with the given network. The format of shape should be (channel, image_width, image_height).
input_bound (Union[tuple, list]) – The pixel range of original images, which should be like [minimum_pixel, maximum_pixel] or (minimum_pixel, maximum_pixel).
loss_weights (Union[list, tuple]) – Weights of three sub-loss in InversionLoss, which can be adjusted to obtain better results. Default: (1, 0.2, 5).
- Raises
TypeError – If the type of network is not Cell.
ValueError – If any value of input_shape is not positive int.
ValueError – If any value of loss_weights is not positive value.
Examples
>>> import mindspore.ops.operations as P >>> from mindspore.nn import Cell >>> from mindarmour.privacy.evaluation.inversion_attack import ImageInversionAttack >>> class Net(Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() ... self._reduce = P.ReduceSum() ... self._squeeze = P.Squeeze(1) ... def construct(self, inputs): ... out = self._softmax(inputs) ... out = self._reduce(out, 2) ... return self._squeeze(out) >>> net = Net() >>> original_images = np.random.random((2,1,10,10)).astype(np.float32) >>> target_features = np.random.random((2,10)).astype(np.float32) >>> inversion_attack = ImageInversionAttack(net, ... input_shape=(1, 10, 10), ... input_bound=(0, 1), ... loss_weights=[1, 0.2, 5]) >>> inversion_images = inversion_attack.generate(target_features, iters=10) >>> evaluate_result = inversion_attack.evaluate(original_images, inversion_images)
- evaluate(original_images, inversion_images, labels=None, new_network=None)[source]
Evaluate the quality of inverted images by three index: the average L2 distance and SSIM value between original images and inversion images, and the average of inverted images’ confidence on true labels of inverted inferred by a new trained network.
- Parameters
original_images (numpy.ndarray) – Original images, whose shape should be (img_num, channels, img_width, img_height).
inversion_images (numpy.ndarray) – Inversion images, whose shape should be (img_num, channels, img_width, img_height).
labels (numpy.ndarray) – Ground truth labels of original images. Default: None.
new_network (Cell) – A network whose structure contains all parts of self._network, but loaded with different checkpoint file. Default: None.
- Returns
float, l2 distance.
float, average ssim value.
Union[float, None], average confidence. It would be None if labels or new_network is None.
- generate(target_features, iters=100)[source]
Reconstruct images based on target_features.
- Parameters
target_features (numpy.ndarray) – Deep representations of original images. The first dimension of target_features should be img_num. It should be noted that the shape of target_features should be (1, dim2, dim3, …) if img_num equals 1.
iters (int) – iteration times of inversion attack, which should be positive integers. Default: 100.
- Returns
numpy.ndarray, reconstructed images, which are expected to be similar to original images.
- Raises
TypeError – If the type of target_features is not numpy.ndarray.
ValueError – If any value of iters is not positive int.Z
- class mindarmour.privacy.evaluation.MembershipInference(model, n_jobs=- 1)[source]
Proposed by Shokri, Stronati, Song and Shmatikov, membership inference is a grey-box attack for inferring user’s privacy data. It requires loss or logits results of the training samples. (Privacy refers to some sensitive attributes of a single user).
For details, please refer to the Using Membership Inference to Test Model Security.
- Parameters
model (Model) – Target model.
n_jobs (int) – Number of jobs run in parallel. -1 means using all processors, otherwise the value of n_jobs must be a positive integer.
- Raises
TypeError – If type of model is not mindspore.train.Model.
TypeError – If type of n_jobs is not int.
ValueError – The value of n_jobs is neither -1 nor a positive integer.
Examples
>>> import mindspore.ops.operations as P >>> from mindspore.nn import Cell >>> from mindspore import Model >>> from mindarmour.privacy.evaluation import MembershipInference >>> def dataset_generator(): ... batch_size = 16 ... batches = 1 ... data = np.random.randn(batches * batch_size,1,10).astype(np.float32) ... label = np.random.randint(0,10, batches * batch_size).astype(np.int32) ... for i in range(batches): ... yield data[i*batch_size:(i+1)*batch_size], label[i*batch_size:(i+1)*batch_size] >>> class Net(Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() ... self._Dense = nn.Dense(10,10) ... self._squeeze = P.Squeeze(1) ... def construct(self, inputs): ... out = self._softmax(inputs) ... out = self._Dense(out) ... return self._squeeze(out) >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True) >>> opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> model = Model(network=net, loss_fn=loss, optimizer=opt) >>> inference_model = MembershipInference(model, 2) >>> config = [{ ... "method": "KNN", ... "params": {"n_neighbors": [3, 5, 7],} ... }] >>> ds_train = ds.GeneratorDataset(dataset_generator, ["image", "label"]) >>> ds_test = ds.GeneratorDataset(dataset_generator, ["image", "label"]) >>> inference_model.train(ds_train, ds_test, config) >>> metrics = ["precision", "accuracy", "recall"] >>> eval_train = ds.GeneratorDataset(dataset_generator, ["image", "label"]) >>> eval_test = ds.GeneratorDataset(dataset_generator, ["image", "label"]) >>> result = inference_model.eval(eval_train. eval_test, metrics) >>> print(result)
- eval(dataset_train, dataset_test, metrics)[source]
Evaluate the different privacy of the target model. Evaluation indicators shall be specified by metrics.
- Parameters
- Returns
list, each element contains an evaluation indicator for the attack model.
- train(dataset_train, dataset_test, attack_config)[source]
Depending on the configuration, use the input dataset to train the attack model. Save the attack model to self._attack_list.
- Parameters
dataset_train (mindspore.dataset) – The training dataset for the target model.
dataset_test (mindspore.dataset) – The test set for the target model.
attack_config (Union[list, tuple]) – Parameter setting for the attack model. The format is [{“method”: “knn”, “params”: {“n_neighbors”: [3, 5, 7]}}, {“method”: “lr”, “params”: {“C”: np.logspace(-4, 2, 10)}}]. The support methods are knn, lr, mlp and rf, and the params of each method must within the range of changeable parameters. Tips of params implement can be found below: KNN, LR, RF, MLP.
- Raises