As synthetic media becomes progressively more realistic and barriers to using
it continue to lower, the technology has been increasingly utilized for
malicious purposes, from financial fraud to nonconsensual pornography. Today,
the principal defense against being misled by synthetic media relies on the
ability of the human observer to visually and auditorily discern between real
and fake. However, it remains unclear just how vulnerable people actually are
to deceptive synthetic media in the course of their day to day lives. We
conducted a perceptual study with 1276 participants to assess how accurate
people were at distinguishing synthetic images, audio only, video only, and
audiovisual stimuli from authentic. To reflect the circumstances under which
people would likely encounter synthetic media in the wild, testing conditions
and stimuli emulated a typical online platform, while all synthetic media used
in the survey was sourced from publicly accessible generative AI technology.
We find that overall, participants struggled to meaningfully discern between
synthetic and authentic content. We also find that detection performance
worsens when the stimuli contains synthetic content as compared to authentic
content, images featuring human faces as compared to non face objects, a single
modality as compared to multimodal stimuli, mixed authenticity as compared to
being fully synthetic for audiovisual stimuli, and features foreign languages
as compared to languages the observer is fluent in. Finally, we also find that
prior knowledge of synthetic media does not meaningfully impact their detection
performance. Collectively, these results indicate that people are highly
susceptible to being tricked by synthetic media in their daily lives and that
human perceptual detection capabilities can no longer be relied upon as an
effective counterdefense.

合成媒体对人类日常生活具有很高的欺骗性，人类的感知检测能力不再是有效的对抗手段。

AI 产生的图像、视频、音频和音视频刺激的人类检测与抛硬币一样好

As Good As A Coin Toss Human detection of AI-generated images, videos,  audio, and audiovisual stimuli

Underwater image enhancement has been attracting much attention due to its
significance in marine engineering and aquatic robotics. Numerous underwater
image enhancement algorithms have been proposed in the last few years. However,
these algorithms are mainly evaluated using either synthetic datasets or few
selected real-world images. It is thus unclear how these algorithms would
perform on images acquired in the wild and how we could gauge the progress in
the field. To bridge this gap, we present the first comprehensive perceptual
study and analysis of underwater image enhancement using large-scale real-world
images. In this paper, we construct an Underwater Image Enhancement Benchmark
(UIEB) including 950 real-world underwater images, 890 of which have the
corresponding reference images. We treat the rest 60 underwater images which
cannot obtain satisfactory reference images as challenging data. Using this
dataset, we conduct a comprehensive study of the state-of-the-art underwater
image enhancement algorithms qualitatively and quantitatively. In addition, we
propose an underwater image enhancement network (called Water-Net) trained on
this benchmark as a baseline, which indicates the generalization of the
proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark
evaluations and the proposed Water-Net demonstrate the performance and
limitations of state-of-the-art algorithms, which shed light on future research
in underwater image enhancement. The dataset and code are available at
this https URL

本文通过对海底图像进行感知研究来评估现有算法在实际数据中的表现，并提出了一个海底图像增强基准 (UIEB) 数据集，包括 950 张真实图像以及涉及卷积神经网络的算法，以此来评估现有算法的性能和局限性。