论文标题
光谱分布意识图像产生
Spectral Distribution Aware Image Generation
论文作者
论文摘要
深层生成模型的最新进展导致了高质量的视觉结果。这样的模型学会从给定的训练分布中生成数据，以使生成的图像不容易被人眼与真实图像区分开。然而，最近在检测出这种假图像的工作表明，它们实际上很容易通过频谱中的伪像可以区分。在本文中，我们建议通过使用光谱歧视器来根据真实数据的频率分布生成图像。所提出的判别器是轻巧的，模块化的，并且可以稳定地与不同常用的GAN损失一起工作。我们表明，所得模型可以更好地生成具有逼真的频谱的图像，因此很难通过该提示来检测。
Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake images pointed out that they are actually easily distinguishable by artifacts in their frequency spectra. In this paper, we propose to generate images according to the frequency distribution of the real data by employing a spectral discriminator. The proposed discriminator is lightweight, modular and works stably with different commonly used GAN losses. We show that the resulting models can better generate images with realistic frequency spectra, which are thus harder to detect by this cue.
