Maybe simply using an objective video quality measurement against the original picture would do the trick. Something like PSNR or SSIM. A "deepfake" is likely to score low on such a score that doesn't depend on high level visual perception.
Also know that you can "deepfake" yourself using a traditional video encoder, just change the keyframe to someone else's face. Of course, it will look broken and totally unconvincing but because of motion compensation, you can sort of map the movement of your face on someone else's face.
The technique in the paper simply has way better motion compensation, so good that it still works if you change the keyframe. Traditional video compression algorithms don't work like that because they are not just for talking faces and can't use such advanced techniques for performance and ease of implementation reasons.
Also know that you can "deepfake" yourself using a traditional video encoder, just change the keyframe to someone else's face. Of course, it will look broken and totally unconvincing but because of motion compensation, you can sort of map the movement of your face on someone else's face.
The technique in the paper simply has way better motion compensation, so good that it still works if you change the keyframe. Traditional video compression algorithms don't work like that because they are not just for talking faces and can't use such advanced techniques for performance and ease of implementation reasons.