[bko]

The way general adversarial networks work on tricking image recognition systems is that they vary pixels of the input image slightly to manipulate the output of the neural network.

For alphazero, the input is the board, which you can't manipulate arbitrarily. You can run an evaluation of a board based on a move and see if its significantly different than the evaluation that alphazero comes up with, and maybe try to exploit that. But if you have a better evaluation of some state than that of alphazero, you're likely a stronger player anyway so this extra step is unnecessary. Most of the value of the bot comes from the evaluation function of a board, along with some hyper-parameters. But the evaluation is probably the most important part and the most difficult to replicate.