Markov processes for enhanced deepfake generation and detection

We investigate both new and existing methods for generating, and especially detecting, deepfakes through the simple but informative task of authenticating binary coin flip data. The main contribution is the introduction of a Markov observation model (MOM) as an alternative probabilistic framework for both deepfake generation and discrimination. Its performance is compared against several existing approaches, such as generative adversarial networks (GANs), support vector machines (SVMs), branching particle filtering (BPF), and human alternatives. Since SVMs are discriminative methods and do not have generative abilities, they are only evaluated for the detection task, while the remaining approaches are assessed on both generation and discrimination. Across the experiments, human participants are shown to perform the worst, which demonstrates the difficulty of reliably identifying deepfaked sequences by a human eye. Among the computational methods, GANs perform better than humans, but are outperformed by SVMs, which in turn are surpassed by BPF. The strongest overall performance comes from the proposed MOM approach, which achieves the best results for deepfake detection out of all methods. A similar result is observed for the generation task, with MOM again showing the strongest performance, followed by BPF, GAN, and humans. These results showcase the generative and discrimination abilities of the proposed method.