Manipulating hidden-Markov-model inferences by corrupting batch data

arXiv:2402.13287v1 Announce Type: cross
Abstract: Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker's inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov model inferences via corrupted data. In particular, we provision a suite of corruption problems for filtering, smoothing, and decoding inferences leveraging an adversarial risk analysis approach. Multiple …

abstract adversarial adversarial machine learning arxiv cs.ai cs.cr cs.lg data decision hidden inference inferences machine machine learning maker manipulation markov novel perspective research series type