Markov Observation Models and Deepfakes

Michael A. Kouritzin ()
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Michael A. Kouritzin: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada

Mathematics, 2025, vol. 13, issue 13, 1-25

Abstract: Herein, expanded Hidden Markov Models (HMMs) are considered as potential deepfake generation and detection tools. The most specific model is the HMM, while the most general is the pairwise Markov chain (PMC). In between, the Markov observation model (MOM) is proposed, where the observations form a Markov chain conditionally on the hidden state. An expectation-maximization (EM) analog to the Baum–Welch algorithm is developed to estimate the transition probabilities as well as the initial hidden-state-observation joint distribution for all the models considered. This new EM algorithm also includes a recursive log-likelihood equation so that model selection can be performed (after parameter convergence). Once models have been learnt through the EM algorithm, deepfakes are generated through simulation, while they are detected using the log-likelihood. Our three models were compared empirically in terms of their generative and detective ability. PMC and MOM consistently produced the best deepfake generator and detector, respectively.

Keywords: Markov observation models; hidden Markov model; Baum–Welch algorithm; expectation-maximization; pairwise Markov chain; deepfake (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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