Recent advances in diffusion models have led to a quantum leap in the quality
of generative visual content. However, quantification of realism of the content
is still challenging. Existing evaluation metrics, such as Inception Score and
Fr\'echet inception distance, fall short on benchmarking diffusion models due
to the versatility of the generated images. Moreover, they are not designed to
quantify realism of an individual image. This restricts their application in
forensic image analysis, which is becoming increasingly important in the
emerging era of generative models. To address that, we first propose a metric,
called Image Realism Score (IRS), computed from five statistical measures of a
given image. This non-learning based metric not only efficiently quantifies
realism of the generated images, it is readily usable as a measure to classify
a given image as real or fake. We experimentally establish the model- and
data-agnostic nature of the proposed IRS by successfully detecting fake images
generated by Stable Diffusion Model (SDM), Dalle2, Midjourney and BigGAN.
We further leverage this attribute of our metric to minimize an IRS-augmented
generative loss of SDM, and demonstrate a convenient yet considerable quality
improvement of the SDM-generated content with our modification. Our efforts
have also led to Gen-100 dataset, which provides 1,000 samples for 100 classes
generated by four high-quality models. We will release the dataset and code.

通过五个统计量计算的图像逼真度评分（IRS）是一个高效的图像逼真度度量，也可用作判断图像真实与否的指标。实验结果表明，IRS 可以成功检测由多种高质量生成模型生成的假图像，并且可以通过改进模型的生成损失函数，显著提高生成内容的质量。

关于量化和改善扩散生成图像的逼真度

On quantifying and improving realism of images generated with diffusion

In many applications of forensic image analysis, state-of-the-art results are
nowadays achieved with machine learning methods. However, concerns about their
reliability and opaqueness raise the question whether such methods can be used
in criminal investigations. So far, this question of legal compliance has
hardly been discussed, also because legal regulations for machine learning
methods were not defined explicitly. To this end, the European Commission
recently proposed the artificial intelligence (AI) act, a regulatory framework
for the trustworthy use of AI. Under the draft AI act, high-risk AI systems for
use in law enforcement are permitted but subject to compliance with mandatory
requirements. In this paper, we review why the use of machine learning in
forensic image analysis is classified as high-risk. We then summarize the
mandatory requirements for high-risk AI systems and discuss these requirements
in light of two forensic applications, license plate recognition and deep fake
detection. The goal of this paper is to raise awareness of the upcoming legal
requirements and to point out avenues for future research.

本研究回顾并总结了机器学习在法证图像分析中的高风险性质，概述了高风险人工智能系统的强制要求，并讨论了这些要求在两个法证应用（车牌识别和深度伪造检测）中的实际应用。目的是提高人们对即将发布的法律要求的认识，并指出未来研究的方向。