论文标题
劫持：意想不到的，验证的黑盒gan
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs
论文作者
论文摘要
尽管生成的对抗网络（GAN）表现出提高的性能，而现实主义的水平与自然图像无法区分，但对数据和计算的需求也很高。我们表明，最新的GAN模型（例如研究人员和行业公开发布）可用于无条件形象生成以外的一系列应用。我们通过迭代方案实现了这一目标，该方案还允许在最新的GAN模型的高度非线性潜在空间中获得对图像生成过程的控制。我们证明，这打开了重复使用最先进的，难以训练的，预先训练的gan的可能性，即使仅授予Black-Box访问权限，也可以具有很高的控制。我们的工作还引起了人们的关注，并意识到，已发表的GAN模型的用例很可能超出了创作者的意图，在完整的公开发布之前，需要考虑到这些案例的意图。代码可从https://github.com/hui-po-wang/hijackgan获得。
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/hui-po-wang/hijackgan.
