With the recent progress in Generative Adversarial Networks (GANs), it is imperative for media and visual forensics to develop detectors which can identify and attribute images to the model generating them. Existing works have shown to attribute images to their corresponding GAN sources with high accuracy. However, these works are limited to a closed set scenario, failing to generalize to GANs unseen during train time and are therefore, not scalable with a steady influx of new GANs. We present an iterative algorithm for discovering images generated from previously unseen GANs by exploiting the fact that all GANs leave distinct fingerprints on their generated images. Our algorithm consists of multiple components including network training, out-of-distribution detection, clustering, merge and refine steps. Through extensive experiments, we show that our algorithm discovers unseen GANs with high accuracy and also generalizes to GANs trained on unseen real datasets. We additionally apply our algorithm to attribution and discovery of GANs in an online fashion as well as to the more standard task of real/fake detection. Our experiments demonstrate the effectiveness of our approach to discover new GANs and can be used in an open-world setup.
翻译:随着Generation Aversarial Networks(GANs)最近的进展,媒体和视觉法证机构必须开发能够识别图像并将图像归属于生成模型的探测器,现有的工程显示将图像归属于相应的GAN来源,并且具有很高的准确性;然而,这些工程仅限于封闭的一套假设,未能在火车时间里将GANs概括为看不见的GANs,因此,由于新的GANs的不断流入,无法进行伸缩。我们通过利用所有GANs在其生成的图像上留下不同指纹这一事实,为发现从以前的GANs产生的图像提供了一种迭代算法。我们的算法由多个组成部分组成,包括网络培训、分配以外的检测、集群、合并和完善步骤。通过广泛的实验,我们显示我们的算法发现了高度精确的、也概括了在无形数据组上受过训练的GANs。我们还运用了我们的算法,将GANs的归属和发现用于在线方式,以及更标准的真实/假探测任务。我们的实验表明我们发现新的GANs的方法的有效性,可以在开放的世界中加以利用。