论文标题
通过指纹结构域中的数据增强来检测一般gan生成的图像检测
General GAN-generated image detection by data augmentation in fingerprint domain
论文作者
论文摘要
在这项工作中，我们通过在指纹结构域中执行数据增强来调查提高GAN生成图像探测器的普遍性。具体而言，我们首先使用基于自动编码器的GAN指纹提取器，然后将指纹的指纹和内容分开，然后进行指纹的随机扰动。然后，原始的指纹用扰动的指纹代替并添加到原始内容中，以产生视觉上不变但具有不同指纹的图像。受干扰的图像可以成功模仿不同gan产生的图像，以改善检测器的概括，这是通过光谱可视化证明的。据我们所知，我们是第一个在指纹域中进行数据增强的人。我们的工作探讨了一种与以前有关空间和频域增强的作品不同的新型前景。与最先进的方法相比，广泛的横gan实验证明了我们方法在检测未知gan产生的假图像中的有效性。
In this work, we investigate improving the generalizability of GAN-generated image detectors by performing data augmentation in the fingerprint domain. Specifically, we first separate the fingerprints and contents of the GAN-generated images using an autoencoder based GAN fingerprint extractor, followed by random perturbations of the fingerprints. Then the original fingerprints are substituted with the perturbed fingerprints and added to the original contents, to produce images that are visually invariant but with distinct fingerprints. The perturbed images can successfully imitate images generated by different GANs to improve the generalization of the detectors, which is demonstrated by the spectra visualization. To our knowledge, we are the first to conduct data augmentation in the fingerprint domain. Our work explores a novel prospect that is distinct from previous works on spatial and frequency domain augmentation. Extensive cross-GAN experiments demonstrate the effectiveness of our method compared to the state-of-the-art methods in detecting fake images generated by unknown GANs.
