Deep neural networks (DNNs) have been applied in several useful services, such as image recognition, intrusion detection, and pattern analysis of machine learning tasks. Recently proposed adversarial examples-slightly modified data that lead to incorrect classification are a severe threat to the security of DNNs. In some situations, however, an adversarial example might be useful, such as when deceiving an enemy classifier on the battlefield. In such a scenario, it is necessary that a friendly classifier not be deceived. In this paper, we propose a friend-safe adversarial example, meaning that the friendly machine can classify the adversarial example correctly. To produce such examples, a transformation is carried out to minimize the probability of incorrect classification by the friend and that of correct classification by the adversary. We suggest two configurations for the scheme: targeted and untargeted class attacks. We performed experiments with this scheme using the MNIST and CIFAR10 datasets. Our proposed method shows a 100% attack success rate and 100% friend accuracy with only a small distortion: 2.18 and 1.54 for the two respective MNIST configurations, and 49.02 and 27.61 for the two respective CIFAR10 configurations. Additionally, we propose a new covert channel scheme and a mixed battlefield application for consideration in further applications. (C) 2018 Elsevier Ltd. All rights reserved.
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