Images such as this one are produced using generative adversial network, which is build from two models:
- one to generate images given a random vector as input
- another trying to detect the generated image from two images, with one of them being real
Then the weights of the first model are updated if the second one detected which image is artificial, and the second model is updated if its prediction is wrong.
Of course you might build a model that can sometime detect AI generated images, but it is probably not possible to differentiate them all the time. Then, if you build such model that is better than any other model to detect generated images, it is possible to create another model trained to fool it.