Feature extraction from sequence of images with Siamese Neural Network
Which is better single frame similarly check or sequence check?

Sequence Check. Because in the Single frame Similarity check (Consider SNN), You need to input pairwise images. So that you need so many pairwise images for your SNN for your Batch Generation.
For the Batch Generation idea is to make usuable batches for training the network. We need to create parallel inputs for the A and B images where the output is the distance. Here we make the naive assumption that if images are in the same group the similarity is 1 otherwise it is 0. If we randomly selected all of the images we would likely end up with most images in different groups.
So no problem if you chose randomly or not.
For the Sequence Check, I picked this paper : Deepfake Video Detection Using Recurrent Neural Networks

Here propose a two-stage analysis composed of a CNN to extract features at the frame level followed by a temporally-aware RNN network to capture temporal inconsistencies between frames. And in this paper, they actually detect deepfake from the videos. They used 600 videos to evaluate the proposed method and get 94% more accuracy.