Markov Processes for Enhanced Deepfake Generation and Detection

New and existing methods for generating, and especially detecting, deepfakes are investigated and compared on the simple problem of authenticating coin flip data. Importantly, an alternative approach to deepfake generation and detection, which uses a Markov Observation Model (MOM) is introduced and compared on detection ability to the traditional Generative Adversarial Network (GAN) approach as well as Support Vector Machine (SVM), Branching Particle Filtering (BPF) and human alternatives. MOM was also compared on generative and discrimination ability to GAN, filtering and humans (as SVM does not have generative ability). Humans are shown to perform the worst, followed in order by GAN, SVM, BPF and MOM, which was the best at the detection of deepfakes. Unsurprisingly, the order was maintained on the generation problem with removal of SVM as it does not have generation ability.

💡 Research Summary

The paper “Markov Processes for Enhanced Deepfake Generation and Detection” investigates both the creation and detection of deepfakes within a highly controlled experimental setting: binary coin‑flip sequences. By reducing the problem to a simple, analytically tractable domain, the authors can directly compare a newly proposed Markov Observation Model (MOM) against several established baselines—Generative Adversarial Networks (GAN), Support Vector Machines (SVM), Branching Particle Filters (BPF), and human judgment.

Data Generation
Five data sources are constructed: (1) truly random sequences generated by Python’s random module (treated as “real”), (2) 137 sequences of length 200 produced by 15 university students attempting to mimic randomness (human “fakes”), (3) two algorithmic deepfake generators derived from prior work