With the digital imagery landscape rapidly evolving, image stocks and
AI-generated image marketplaces have become central to visual media.
Traditional stock images now exist alongside innovative platforms that trade in
prompts for AI-generated visuals, driven by sophisticated APIs like DALL-E 3
and Midjourney. This paper studies the possibility of employing multi-modal
models with enhanced visual understanding to mimic the outputs of these
platforms, introducing an original attack strategy. Our method leverages
fine-tuned CLIP models, a multi-label classifier, and the descriptive
capabilities of GPT-4V to create prompts that generate images similar to those
available in marketplaces and from premium stock image providers, yet at a
markedly lower expense. In presenting this strategy, we aim to spotlight a new
class of economic and security considerations within the realm of digital
imagery. Our findings, supported by both automated metrics and human
assessment, reveal that comparable visual content can be produced for a
fraction of the prevailing market prices ($0.23 - $0.27 per image), emphasizing
the need for awareness and strategic discussions about the integrity of digital
media in an increasingly AI-integrated landscape. Our work also contributes to
the field by assembling a dataset consisting of approximately 19 million
prompt-image pairs generated by the popular Midjourney platform, which we plan
to release publicly.

利用多模态模型与增强的视觉理解来模仿 AI 生成图像市场，并引入一种新的攻击策略，结果表明可以以显著较低的成本生成与市场价格相似的视觉内容，突出了数字媒体完整性的需求和战略讨论。

迭代地提示多模态 LLMs 以重现自然和 AI 生成的图像

Iteratively Prompting Multimodal LLMs to Reproduce Natural and  AI-Generated Images

The United States spends more than $1B each year on initiatives such as the
American Community Survey (ACS), a labor-intensive door-to-door study that
measures statistics relating to race, gender, education, occupation,
unemployment, and other demographic factors. Although a comprehensive source of
data, the lag between demographic changes and their appearance in the ACS can
exceed half a decade. As digital imagery becomes ubiquitous and machine vision
techniques improve, automated data analysis may provide a cheaper and faster
alternative. Here, we present a method that determines socioeconomic trends
from 50 million images of street scenes, gathered in 200 American cities by
Google Street View cars. Using deep learning-based computer vision techniques,
we determined the make, model, and year of all motor vehicles encountered in
particular neighborhoods. Data from this census of motor vehicles, which
enumerated 22M automobiles in total (8% of all automobiles in the US), was used
to accurately estimate income, race, education, and voting patterns, with
single-precinct resolution. (The average US precinct contains approximately
1000 people.) The resulting associations are surprisingly simple and powerful.
For instance, if the number of sedans encountered during a 15-minute drive
through a city is higher than the number of pickup trucks, the city is likely
to vote for a Democrat during the next Presidential election (88% chance);
otherwise, it is likely to vote Republican (82%). Our results suggest that
automated systems for monitoring demographic trends may effectively complement
labor-intensive approaches, with the potential to detect trends with fine
spatial resolution, in close to real time.

本文提出了一种使用深度学习和机器视觉技术从 Google Street View 车辆在美国 200 个城市的街道场景中获取汽车数量信息，进而估计美国社会经济趋势和社会人口统计的方法，结果表明自动化系统可以有效补充劳动密集型方法，并具有实时感。