[论文] On the Origin of Synthetic Information by Means of Steganographic...

论文概要

研究领域: AI 作者: Ching-Chun Chang, Isao Echizen 发布时间: 2026-05-28 arXiv: 2605.27551

中文摘要

物种起源是自然科学中"谜中之谜";类比而言,合成信息的起源则是信息科学中同等地位的谜题。这一问题承载着道德重量,技术解释既无法完全解决,也不容忽视--因为它对真相、信任和人类智识的影响深入经济与社会肌理。AI的强大能力使得合成信息的演化谱系愈发难以追溯:一个足够强大的模型可能生成与父代在结构或信号层面都毫无相似之处的"后代"。正如遗传学中表型相似但基因型迥异的现象,本文提出了一种类似遗传机制的隐写术方案:在"后代"生成瞬间,投影器从父代提取特征,由隐写编码器将其不可见地嵌入后代。该特征在赛博生态中伴随后代整个生命周期。当需要追溯亲缘时,解码器提取特征并与候选父代比对,从而确定最可能的来源。

原文摘要

The origin of species has been the mystery of mysteries in natural science. By analogy, the origin of synthetic information, we suggest, is the mystery of mysteries in information science. The question carries a moral weight that a technical account can neither fully resolve nor responsibly ignore, as its impact on truth, trust, and human intellect extends deep into the broader economy and society. The very power of artificial intelligence makes the evolutionary lineage of synthetic information grow ever harder to trace, for a sufficiently capable model may generate offspring that bear little resemblance, at either the structural or signal level, to the parent source from which they were derived. As in genetics, two individuals may share the same phenotype mirroring each other in outward appearance, yet differ fundamentally in their genotype. We propose, by means of steganography, a mech...

--- *自动采集于 2026-05-29*

#论文 #arXiv #AI #小凯