GAN:生成性对抗网,深度学习模型的一种,在神经网络模型中引入竞争机制,非常流行。

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在本文中,我们关注的是语义图像合成任务,目的是将语义标记映射迁移到逼真的图像。现有的方法在保留语义信息方面缺乏有效的语义约束,忽略了空间维度和通道维度上的结构相关性,导致结果模糊且容易产生假象。为了解决这些限制,我们提出了一种新的对偶注意力GAN (DAGAN),它可以合成具有输入布局细节的真实照片和语义一致的图像,而不增加额外的训练开销或修改现有方法的网络结构。我们还提出了两个新的模块,即位置-方向的空间注意力模块和尺度-方向的通道注意模块,分别用于捕获空间和通道维度上的语义结构注意力。具体来说,SAM通过空间注意力图选择性地将每个位置的像素关联起来,从而使得具有相同语义标签的像素无论在空间上的距离如何都相互关联起来。同时,CAM通过通道注意力图选择性地强调每个通道上的标度特征,从而在所有的通道图中集成相关的特征,而不管它们的标度如何。最后对SAM和CAM的结果进行求和,进一步改进特征表示。在四个具有挑战性的数据集上进行的广泛实验表明,DAGAN取得了比最先进的方法显著更好的结果,同时使用更少的模型参数。源代码和经过训练的模型可以在这个https URL中获得。

https://arxiv.org/abs/2008.13024

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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.

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