Artificial, CNN-generated images are now of such high quality that humans
have trouble distinguishing them from real images. Several algorithmic
detection methods have been proposed, but these appear to generalize poorly to
data from unknown sources, making them infeasible for real-world scenarios. In
this work, we present a framework for evaluating detection methods under
real-world conditions, consisting of cross-model, cross-data, and
post-processing evaluation, and we evaluate state-of-the-art detection methods
using the proposed framework. Furthermore, we examine the usefulness of
commonly used image pre-processing methods. Lastly, we evaluate human
performance on detecting CNN-generated images, along with factors that
influence this performance, by conducting an online survey. Our results suggest
that CNN-based detection methods are not yet robust enough to be used in
real-world scenarios.

研究表明，基于 CNN 的检测方法在现实场景中的鲁棒性还不足，本研究提出了一个跨模型、跨数据和后处理评估框架，并评估了最先进的检测方法，同时还探讨了常用的图像预处理方法及人类对于 CNN 生成图像的检测表现及影响因素。

在真实场景中检测卷积神经网络生成的人脸图像

Detecting CNN-Generated Facial Images in Real-World Scenarios

We examine how various types of noise in the parallel training data impact
the quality of neural machine translation systems. We create five types of
artificial noise and analyze how they degrade performance in neural and
statistical machine translation. We find that neural models are generally more
harmed by noise than statistical models. For one especially egregious type of
noise they learn to just copy the input sentence.

本文探讨了并行训练数据中各种类型的噪声对神经机器翻译系统性能的影响，通过创造并分析五种人造噪声的方式来研究神经机器翻译和统计机器翻译的性能下降，发现神经模型通常比统计模型更容易受到噪声的影响。