Robust Adversarial Immune-Inspired Learning System

A computer-implemented method of classifying an input using a deep learning system, comprising:

• receiving, by a computer processor, a first input for a deep learning system, where the deep learning system was trained with a training dataset and the training dataset includes data for a plurality of classes;
• for each class in the training dataset, identifying, by the computer processor, a set of data points in the training dataset, where the data points in the set of data points are similar to the first input;
• for each set of identified data points, generating, by the computer processor, additional data points from data points in the set of identified data points using genetic operators, where the identified data points and the additional data points collectively form a pool of data points;
• for each of the data points in the pool of data points, calculating, by the computer processor, a similarity score in relation to the first input;
• selecting, by the computer processor, a subset of data points with the highest similarity scores amongst the data points in the pool of data points;
• appending, by the computer processor, the data points in the subset of data points to the training dataset;
• receiving, by the computer processor, a second input for the deep learning system;
• for each class in the training dataset, identifying, by the computer processor, a second set of data points in the training dataset, where the data points in the second set of data points are similar to the second input;
• for each second set of data points, generating, by the computer processor, additional data points from data points in the second set of data points using genetic operators, where the identified data points and the additional data points collectively form a second pool of data points;
• for each of the data points in the second pool of data points, calculating, by the computer processor, a similarity score in relation to the second input;
• selecting, by the computer processor, a subset of data points with the highest similarity scores amongst the data points in the second pool of data points;
• and
• predicting, by the computer processor, a class label for the second input from the plurality of classes, where the prediction of a class label for the second input is determined by consensus of the data points in the second pool of data points with the highest similarity scores.

A deep learning system, comprising:

• a training data set having data from a set of classes;
• a flocking module configured to receive an input for a deep learning system and operates to identify a set of data points in the training dataset for each class in the set of classes, where the data points in the set of data points are similar to the input;
• for each set of data points, an expansion module generates additional data points from the data points in a given set of data points using genetic operators, where each additional data point is tagged with class inherited from its parents;
• for each of the data points, an optimizer module calculates a similarity score in relation to the input and selects a subset of data points with the highest similarity scores amongst the data points;
• and
• a predictor module predicts a class label for the input from the plurality of classes, where the prediction of a class label for the input is determined by consensus of the data points in the subset of data points with the highest similarity scores.