Main Content

verifyNetworkRobustness

Verify adversarial robustness of deep learning network

Since R2022b

collapse all in page

    Syntax

    result = verifyNetworkRobustness(net,XLower,XUpper,label)
    result = verifyNetworkRobustness(___,Name=Value)

    Description

    result = verifyNetworkRobustness(net,XLower,XUpper,label) verifies whether the network net is adversarially robust with respect to the class label when the input is between XLower and XUpper. For more information, see Adversarial Examples.

    A network is robust to adversarial examples for a specific input if the predicted class does not change when the input is perturbed between XLower and XUpper. For more information, see Algorithms.

    The verifyNetworkRobustness function requires the Deep Learning Toolbox Verification Library support package. If this support package is not installed, use the Add-On Explorer. To open the Add-On Explorer, go to the MATLAB® Toolstrip and click Add-Ons > Get Add-Ons.

    example

    result = verifyNetworkRobustness(___,Name=Value) verifies the adversarial robustness with additional options specified by one or more name-value arguments.

    Examples

    collapse all

    This example uses:

    Open Live Script

    Verify the adversarial robustness of an image classification network.

    Load a pretrained classification network. This network is a dlnetwork object that has been trained to predict the class label of images of handwritten digits.

    load("digitsRobustClassificationConvolutionNet.mat")

    Prepare the network for verification by removing the softmax layer. When you remove layers from a dlnetwork object, the software returns the network as an uninitialized dlnetwork object. To initialize the network, use the initialize function.

    netRobust = removeLayers(netRobust,"softmax");
netRobust = initialize(netRobust);

    Load the test data.

    [XTest,TTest] = digitTest4DArrayData;

    Select the first ten images.

    X = XTest(:,:,:,1:10);
label = TTest(1:10);

    Convert the test data to a dlarray object.

    X = dlarray(X,"SSCB");

    Verify the network robustness to an input perturbation between –0.01 and 0.01 for each pixel. Create lower and upper bounds for the input.

    perturbation = 0.01;
XLower = X - perturbation;
XUpper = X + perturbation;

    Verify the network robustness for each test image.

    result = verifyNetworkRobustness(netRobust,XLower,XUpper,label);
summary(result)
         verified      10 
     violated       0 
     unproven       0

    This example uses:

    Open Live Script

    Find the maximum adversarial perturbation that you can apply to an input without changing the predicted class.

    Load a pretrained classification network. This network is a dlnetwork object that has been trained to predict the class of images of handwritten digits.

    load("digitsRobustClassificationConvolutionNet.mat")

    Prepare the network for verification by removing the softmax layer. When you remove layers from a dlnetwork object, the software returns the network as an uninitialized dlnetwork object. To initialize the network, use the initialize function.

    netRobust = removeLayers(netRobust,"softmax");
netRobust = initialize(netRobust);

    Load the test data.

    [XTest,TTest] = digitTest4DArrayData;

    Select a test image.

    idx = 3;
X = XTest(:,:,:,idx);
label = TTest(idx);

    Create lower and upper bounds for a range of perturbation values.

    perturbationRange = 0:0.005:0.1;

for i = 1:numel(perturbationRange)
XLower(:,:,:,i) = X - perturbationRange(i);
XUpper(:,:,:,i) = X + perturbationRange(i);
end

    Repeat the class label for each set of bounds.

    label = repmat(label,numel(perturbationRange),1);

    Convert the bounds to dlarray objects.

    XLower = dlarray(XLower,"SSCB");
XUpper = dlarray(XUpper,"SSCB");

    Verify the adversarial robustness for each perturbation.

    result = verifyNetworkRobustness(netRobust,XLower,XUpper,label);
plot(perturbationRange,result,"*")
xlabel("Perturbation")

    Figure contains an axes object. The axes object with xlabel Perturbation contains a line object which displays its values using only markers.

    Find the maximum perturbation value for which the function returns verified. 

    maxIdx = find(result=="verified",1,"last");
maxPerturbation = perturbationRange(maxIdx)
    maxPerturbation = 0.0600

    Input Arguments

    collapse all

    Network, specified as an initialized dlnetwork object. To initialize a dlnetwork object, use the initialize function.

    The function supports networks with these layers:

    The function does not support networks with multiple inputs and multiple outputs.

    The function verifies the network using the final layer. For most applications, use the final fully connected layer for verification. If your network has a different layer as its final layer (for example, softmax), remove the layer before calling the function.

    Input lower bound, specified as a formatted dlarray object. For more information about dlarray formats, see the fmt input argument of dlarray.

    The lower and upper bounds, XLower and XUpper, must have the same size and format. The function computes the results across the batch ("B") dimension of the input lower and upper bounds.

    Input upper bound, specified as a formatted dlarray object. For more information about dlarray formats, see the fmt input argument of dlarray.

    The lower and upper bounds, XLower and XUpper, must have the same size and format. The function computes the results across the batch ("B") dimension of the input lower and upper bounds.

    Class label, specified as a numeric index or a categorical, or a vector of these values. The length of label must match the size of the batch ("B") dimension of the lower and upper bounds.

    The function verifies that the predicted class that the network returns matches label for any input in the range defined by the lower and upper bounds.

    Note

    If you specify label as a categorical, then the order of the categories must match the order of the outputs in the network.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | categorical

    Name-Value Arguments

    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Example: verifyNetworkRobustness(net,XLower,XUpper,label,MiniBatchSize=32,ExecutionEnvironment="multi-gpu") verifies the network robustness using a mini-batch size of 32 and using multiple GPUs.

    Since R2023b

    Size of the mini-batch to use when verifying robustness, specified as a positive integer.

    Larger mini-batch sizes require more memory, but can lead to faster computations.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Since R2024a

    Hardware resource for verifying network robustness, specified as one of these values:

    • "auto" – Use a local GPU if one is available. Otherwise, use the local CPU.

    • "cpu" – Use the local CPU.

    • "gpu" – Use the local GPU.

    • "multi-gpu" – Use multiple GPUs on one machine, using a local parallel pool based on your default cluster profile. If there is no current parallel pool, the software starts a parallel pool with pool size equal to the number of available GPUs.

    • "parallel-auto" – Use a local or remote parallel pool. If there is no current parallel pool, the software starts one using the default cluster profile. If the pool has access to GPUs, then only workers with a unique GPU perform the computations and excess workers become idle. If the pool does not have GPUs, then the computations take place on all available CPU workers instead.

    • "parallel-cpu" – Use CPU resources in a local or remote parallel pool, ignoring any GPUs. If there is no current parallel pool, the software starts one using the default cluster profile.

    • "parallel-gpu" – Use GPUs in a local or remote parallel pool. Excess workers become idle. If there is no current parallel pool, the software starts one using the default cluster profile.

    The "gpu", "multi-gpu", "parallel-auto", "parallel-cpu", and "parallel-gpu" options require Parallel Computing Toolbox™. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). If you choose one of these options and Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.

    For more information on when to use the different execution environments, see Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud.

    Output Arguments

    collapse all

    Verification result, returned as a categorical array. For each set of input lower and upper bounds, the function returns the corresponding element of this array as one of these values:

    • "verified" — The network is robust to adversarial inputs between the specified bounds.

    • "violated" — The network is not robust to adversarial inputs between the specified bounds.

    • "unproven" — Unable to prove whether the network is robust to adversarial inputs between the specified bounds.

    The function computes the results across the batch ("B") dimension of the input lower and upper bounds. If you supply k upper bounds, lower bounds, and labels, then result(k) corresponds to the verification result for the kth input lower and upper bounds with respect to label(k). For more information, see Algorithms.

    More About

    collapse all

    Adversarial Examples

    Neural networks can be susceptible to a phenomenon known as adversarial examples [1], where very small changes to an input can cause the network predictions to significantly change. For example, making a small change to an image that causes the network to misclassify it. These changes are often imperceptible to humans.

    For example, this image shows that adding an imperceptible perturbation to an image of peppers means that the classification changes from "bell pepper" to "pillow".

    Diagram of an adversarial example. Adding an imperceptible perturbation to the image causes the model to misclassify it.

    A network is adversarially robust if the output of the network does not change significantly when the input is perturbed. For classification tasks, adversarial robustness means that the output of the fully connected layer with the highest value does not change, and therefore the predicted class does not change.

    Algorithms

    To verify the robustness of a network for an input, the function checks that when the input is perturbed between the specified lower and upper bound, the output does not significantly change.

    Let X be an input with respect to which you want to test the robustness of the network. To use the verifyNetworkRobustness function, you must specify a lower and upper bound for the input. For example, let ϵ be a small perturbation. You can define a lower and upper bound for the input as Xlower=X−ϵ and Xupper=X+ϵ, respectively.

    To verify the adversarial robustness of the network, the function checks that, for all inputs between Xlower and Xupper, no adversarial example exists. To check for adversarial examples, the function uses these steps.

    1. Create an input set using the lower and upper input bounds.

    2. Pass the input set through the network and return an output set. To reduce computational overhead, the function performs abstract interpretation by approximating the output of each layer using the DeepPoly [2] method.

    3. Check if the specified label remains the same for the entire input set. Because the algorithm uses overapproximation when it computes the output set, the result can be unproven if part of the output set corresponds to an adversarial example.

    If you specify multiple pairs of input lower and upper bounds, then the function verifies the robustness for each pair of input bounds.

    Note

    Soundness with respect to floating point: In rare cases, floating-point rounding errors can accumulate which can cause the network output to be outside the computed bounds and the verification results to be different. This can also be true when working with networks you produced using C/C++ code generation.

    References

    [1] Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. “Explaining and Harnessing Adversarial Examples.” Preprint, submitted March 20, 2015. https://arxiv.org/abs/1412.6572.

    [2] Singh, Gagandeep, Timon Gehr, Markus Püschel, and Martin Vechev. “An Abstract Domain for Certifying Neural Networks”. Proceedings of the ACM on Programming Languages 3, no. POPL (January 2, 2019): 1–30. https://dl.acm.org/doi/10.1145/3290354.

    Extended Capabilities

    Version History

    Introduced in R2022b

    expand all

    See Also

    | |

    Topics