  The landscape of fake media creation changed with the introduction of
Generative Adversarial Networks (GAN s). Fake media creation has been on the
rise with the rapid advances in generation technology, leading to new
challenges in Detecting fake media. A fundamental characteristic of GAN s is
their sensitivity to parameter initialization, known as seeds. Each distinct
seed utilized during training leads to the creation of unique model instances,
resulting in divergent image outputs despite employing the same architecture.
This means that even if we have one GAN architecture, it can produce countless
variations of GAN models depending on the seed used. Existing methods for
attributing deepfakes work well only if they have seen the specific GAN model
during training. If the GAN architectures are retrained with a different seed,
these methods struggle to attribute the fakes. This seed dependency issue made
it difficult to attribute deepfakes with existing methods. We proposed a
generalized deepfake attribution network (GDA-N et) to attribute fake images to
their respective GAN architectures, even if they are generated from a retrained
version of the GAN architecture with a different seed (cross-seed) or from the
fine-tuned version of the existing GAN model. Extensive experiments on
cross-seed and fine-tuned data of GAN models show that our method is highly
effective compared to existing methods. We have provided the source code to
validate our results.
