合成摄影检测：识别AI生成合成图像的视觉指南
Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI
AI总结：
针对AI生成的类真实合成照片可能被恶意利用的威胁，本研究聚焦近期生成式扩散模型的可见伪影缺陷，对伪影分类并举例，探讨检测挑战与实际应用，为合成图像识别提供视觉指导。
摘要：
人工智能（AI）工具在生成合成图像方面已变得极为强大。尤其值得关注的是那些酷似照片的生成图像，因为它们意在呈现真实世界事件。从诈骗分子到国家级行为体，各类威胁主体可能恶意利用合成照片来欺骗、欺诈和误导公众。缓解这一威胁通常需要回答一个基本分析问题：这张照片是真实的还是合成的？为解决该问题，我们研究了近期生成式扩散模型的性能，并聚焦于它们的缺陷：生成图像中存在的可见伪影，经训练的人眼可借此识别其合成来源。我们对这些伪影进行了分类，提供了示例，讨论了检测它们的挑战，提出了本研究的实际应用，并概述了未来的研究方向。
英文摘要：
Artificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspire to represent real world events. Synthetic photographs may be used maliciously by a broad range of threat actors, from scammers to nation-state actors, to deceive, defraud, and mislead people. Mitigating this threat usually involves answering a basic analytic question: Is the photograph real or synthetic? To address this, we have examined the capabilities of recent generative diffusion models and have focused on their flaws: visible artifacts in generated images which reveal their synthetic origin to the trained eye. We categorize these artifacts, provide examples, discuss the challenges in detecting them, suggest practical applications of our work, and outline future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
