Hardened deep neural networks through training from adversarial misclassified data

Abstract

Various embodiments are generally directed to techniques for training deep neural networks, such as with an iterative approach, for instance. Some embodiments are particularly directed to a deep neural network (DNN) training system that generates a hardened DNN by iteratively training DNNs with images that were misclassified by previous iterations of the DNN. One or more embodiments, for example, may include logic to generate an adversarial image that is misclassified by a first DNN that was previously trained with a set of sample images. In some embodiments, the logic may determine a second training set that includes the adversarial image that was misclassified by the first DNN and the first training set of one or more sample images. The second training set may be used to train a second DNN. In various embodiments, the above process may be repeated for a predetermined number of iterations to produce a hardened DNN.