The quality of image generation and manipulation is reaching impressive
levels, making it increasingly difficult for a human to distinguish between
what is real and what is fake. However, deep networks can still pick up on the
subtle artifacts in these doctored images. We seek to understand what
properties of fake images make them detectable and identify what generalizes
across different model architectures, datasets, and variations in training. We
use a patch-based classifier with limited receptive fields to visualize which
regions of fake images are more easily detectable. We further show a technique
to exaggerate these detectable properties and demonstrate that, even when the
image generator is adversarially finetuned against a fake image classifier, it
is still imperfect and leaves detectable artifacts in certain image patches.
Code is available at this https URL

通过研究伪造图像的特殊属性，本文提出了一种可以更容易地检测到伪造图像的基于补丁分类器，并演示了即使图像生成器进行对抗性微调，仍然存在可检测的伪造图像噪点的技术。

如何检测出伪造图片？理解具有一般性质的特征

What makes fake images detectable? Understanding properties that  generalize

This paper seeks to develop a deeper understanding of the fundamental
properties of neural text generations models. The study of artifacts that
emerge in machine generated text as a result of modeling choices is a nascent
research area. Previously, the extent and degree to which these artifacts
surface in generated text has not been well studied. In the spirit of better
understanding generative text models and their artifacts, we propose the new
task of distinguishing which of several variants of a given model generated a
piece of text, and we conduct an extensive suite of diagnostic tests to observe
whether modeling choices (e.g., sampling methods, top-$k$ probabilities, model
architectures, etc.) leave detectable artifacts in the text they generate. Our
key finding, which is backed by a rigorous set of experiments, is that such
artifacts are present and that different modeling choices can be inferred by
observing the generated text alone. This suggests that neural text generators
may be more sensitive to various modeling choices than previously thought.

本文提出了一项新任务，即区分生成文本的几个变种中的哪一个，并进行了一系列诊断测试，以观察建模选择（例如取样方法，top-k 概率，模型架构等）是否在生成的文本中留下可检测的痕迹。我们的主要发现是，这些痕迹存在，并且不同的建模选择可以通过观察生成的文本来推断出来，这表明神经文本生成器可能比以前想象的更敏感于各种建模选择。