EVAGAN: Evasion Generative Adversarial Network for Low Data Regimes. (arXiv:2109.08026v6 [cs.CR] UPDATED)

A myriad of recent literary works has leveraged generative adversarial
networks (GANs) to generate unseen evasion samples. The purpose is to annex the
generated data with the original train set for adversarial training to improve
the detection performance of machine learning (ML) classifiers. The quality of
generated adversarial samples relies on the adequacy of training data samples.
However, in low data regimes like medical diagnostic imaging and cybersecurity,
the anomaly samples are scarce in number. This paper proposes a novel …

adversarial data evasion network