Deep fakes are a growing concern  : the ability to credibly alter a video may have great (negative) impacts on our society. It is so much of a concern, that the biggest tech companies launched a specific challenge  : https://deepfakedetectionchallenge.ai/.

However, from what I undestand, most Deep Fake generation techniques rely on the use of adversarial models. One model generate a new image, while another model try to detect if the image is doctored or not. Both model "learn" from being confronted to the other.

That being said, If a good deep fake detection model emerge (from the previously challenge, or not), wouldn't it be rendered unusefull almost instantly by learning from it in an adversarial setting  ?

Deep fakes are a growing concern: the ability to credibly alter a video may have great (negative) impacts on our society. It is so much of a concern, that the biggest tech companies launched a specific challenge: https://deepfakedetectionchallenge.ai/.

However, from what I understand, most deep fake generation techniques rely on the use of adversarial models. One model generates a new image, while another model tries to detect if the image is doctored or not. Both models "learn" from being confronted with the other.

That being said, if a good deep fake detection model emerges (from the previous challenge, or not), wouldn't it be rendered useless almost instantly by learning from it in an adversarial setting?