Verification

Train robust networks and verify network robustness

Deep learning verification is a set of techniques for assessing the properties of deep neural networks. For example, you can verify the robustness properties of a network, compute network output bounds, find adversarial examples, and detect out-of-distribution data.

The Deep Learning Toolbox™ Verification Library support package enables testing robustness properties of deep learning networks.

Use the verifyNetworkRobustness function to verify network robustness to adversarial examples. A network is robust to adversarial inputs if the predicted class of the network does not change when the input is perturbed between specified lower and upper input bounds. For a set of input bounds, the function checks if the network is robust to adversarial examples between those input bounds and returns either verified, violated, or unproven.
Use the estimateNetworkOutputBounds function to estimate the range of output values that the network returns when the input is between specified lower and upper bounds. Use this function to estimate how sensitive the network predictions are to input perturbation.
Use the networkDistributionDiscriminator function to create a distribution discriminator that separates data into in- and out-of-distribution.

Functions

`estimateNetworkOutputBounds`	Estimate output bounds of deep learning network
`verifyNetworkRobustness`	Verify adversarial robustness of deep learning network
`networkDistributionDiscriminator`	Deep learning distribution discriminator
`isInNetworkDistribution`	Determine whether data is within the distribution of the network
`distributionScores`	Distribution confidence scores

Objects

`BaselineDistributionDiscriminator`	Baseline distribution discriminator
`EnergyDistributionDiscriminator`	Energy distribution discriminator
`ODINDistributionDiscriminator`	ODIN distribution discriminator
`HBOSDistributionDiscriminator`	HBOS distribution discriminator

Topics

Verify Robustness of Deep Learning Neural Network
This example shows how to verify the adversarial robustness of a deep learning neural network.
Deep Learning Visualization Methods
Learn about and compare deep learning visualization methods.
Out-of-Distribution Detection for Deep Neural Networks
This example shows how to detect out-of-distribution (OOD) data in deep neural networks.

Featured Examples

Generate Untargeted and Targeted Adversarial Examples for Image Classification

Use the fast gradient sign method (FGSM) and the basic iterative method (BIM) to generate adversarial examples for a pretrained neural network.

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Train Image Classification Network Robust to Adversarial Examples

Train a neural network that is robust to adversarial examples using fast gradient sign method (FGSM) adversarial training.

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Train Robust Deep Learning Network with Jacobian Regularization

Train a neural network that is robust to adversarial examples using a Jacobian regularization scheme [1].

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Generate Adversarial Examples for Semantic Segmentation

Generate adversarial examples for a semantic segmentation network using the basic iterative method (BIM).

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Explore Semantic Segmentation Network Using Grad-CAM

Explore the predictions of a pretrained semantic segmentation network using Grad-CAM.

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Interpret Deep Network Predictions on Tabular Data Using LIME

Use the locally interpretable model-agnostic explanations (LIME) technique to understand the predictions of a deep neural network classifying tabular data. You can use the LIME technique to understand which predictors are most important to the classification decision of a network.

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Out-of-Distribution Data Discriminator for YOLO v4 Object Detector

Detect out-of-distribution (OOD) data in a YOLO v4 object detector.

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