This is how their model was trained, but I think what you've said may not quite be the case. Because the discriminator (D) and generator (G) usually compete in

This is how their model was trained, but I think what you've said may not quite be the case. Because the discriminator (D) and generator (G) usually compete in a minimax game, the equilibrium probability of D correctly classifying an image as fake tends to 1/2 (ignoring distributional factors). If the competing networks have enough capacity and can be stably trained, then in theory they will reach equilibrium as the data distribution from G converges to the actual data distribution. If this is the case, then the discriminator correctly identifies fake videos with a probability of 1/2. They may not reach equilibrium (making D > 0.5), but it's not clear that the discriminator itself is a panacea for identifying fake videos/images.