  While Generative Adversarial Networks (GANs) show increasing performance and
the level of realism is becoming indistinguishable from natural images, this
also comes with high demands on data and computation. We show that
state-of-the-art GAN models -- such as they are being publicly released by
researchers and industry -- can be used for a range of applications beyond
unconditional image generation. We achieve this by an iterative scheme that
also allows gaining control over the image generation process despite the
highly non-linear latent spaces of the latest GAN models. We demonstrate that
this opens up the possibility to re-use state-of-the-art, difficult to train,
pre-trained GANs with a high level of control even if only black-box access is
granted. Our work also raises concerns and awareness that the use cases of a
published GAN model may well reach beyond the creators' intention, which needs
to be taken into account before a full public release. Code is available at
https://github.com/a514514772/hijackgan.
