论文标题
关于使用本福德定律检测甘恩生成的图像
On the use of Benford's law to detect GAN-generated images
论文作者
论文摘要
生成对抗网络（GAN）体系结构的出现使任何人都可以产生令人难以置信的逼真的合成图像。 gan生成的图像的恶意扩散可能会导致严重的社会和政治后果（例如，假新闻传播，舆论形成等）。因此，重要的是通过开发能够检测到的溶液来调节合成图像的广泛分布。在本文中，我们研究了使用本福德定律来区分自然照片中的甘恩生成图像的可能性。本福德定律描述了量化离散余弦变换（DCT）系数的最重要数字的分布。扩展并推广此属性，我们表明可以从图像中提取紧凑的特征向量。该特征向量可以馈送到非常简单的分类器中，以实现GAN生成的图像检测目的。
The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford's law to discriminate GAN-generated images from natural photographs. Benford's law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose.
