International Business Machines Corporation

Kevin Eykholt

Ian M Molloy

Jiyong Jang

Taesung Lee

Douglas Lee Schales

Shiqi Wang

A processor-implemented method generates adversarial example objects. One or more processors represent an adversarial input generation process as a graph. The processor(s) explore the graph, such that a sequence of edges on the graph are explored. The processor(s) create, based on the exploring, an adversarial example object, and utilize the created adversarial example object to harden an existing process model against vulnerabilities.

Graph exploration framework for adversarial example generation

Adaptive verifiable training enables the creation of machine learning models robust with respect to multiple robustness criteria. In general, such training exploits inherent inter-class similarities within input data and enforces multiple robustness criteria based on this information. In particular, the approach exploits pairwise class similarity and improves the performance of a robust model by relaxing robustness constraints for similar classes and increasing robustness constraints for dissimilar classes. Between similar classes, looser robustness criteria (i.e., smaller ∈) are enforced so as to minimize possible overlap when estimating the robustness region during verification. Between dissimilar classes, stricter robustness regions (i.e., larger ∈) are enforced. If pairwise class relationships are not available initially, preferably they are generated by receiving a pre-trained classifier and then applying a clustering algorithm (e.g., agglomerative clustering) to generate them. Once pre-defined or computed pairwise relationships are available, several grouping methods are provided to create classifiers for multiple robustness criteria.

Adaptive robustness certification against adversarial examples

A neural network is augmented to enhance robustness against adversarial attack. In this approach, a fully-connected additional layer is associated with a last layer of the neural network. The additional layer has a lower dimensionality than at least one or more intermediate layers. After sizing the additional layer appropriately, a vector bit encoding is applied. The encoding comprises an encoding vector for each output class. Preferably, the encoding is an n-hot encoding, wherein n represents a hyperparameter. The resulting neural network is then trained to encourage the network to associated features with each of the hot positions. In this manner, the network learns a reduced feature set representing those features that contain a high amount of information with respect to each output class, and/or to learn constraints between those features and the output classes. The trained neural network is used to perform a classification that is robust against adversarial examples.

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Kevin Eykholt