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2026/08/15

乾隆宸翰何真假 How Genuine Is Qianlong’s Imperial Calligraphy?

 


乾隆宸翰何真假

衣若芬

 

走进博物馆,看见一幅古画,上面题着诗,旁边钤满大大小小的印章。许多人第一时间会问:这是不是乾隆皇帝亲笔?

这个问题看似简单,却未必容易回答。

前些时候在台北故宫看展览,面对一件传为乾隆御题的书画作品,我忽然想到:如果今天有人工智能能够百分之百模仿乾隆的笔迹,我们还能一眼分辨真假吗?而如果不能,那么我们究竟在寻找什么样的

乾隆皇帝是中国历史上留下题跋最多的皇帝。有人开玩笑,说他是盖章狂魔,或者是弹幕达人。然而,关于乾隆题字的真假问题,却一直存在。

故宫收藏中,有些作品上的御题被认为出自乾隆亲笔;有些则可能由宫廷朝臣代笔。对于书法鉴定家来说,这当然是重要课题。用笔提顿习惯是否一致?线条是否自然?结体章法是否符合乾隆书风?这些都成为判断的依据。

可是,当我站在展柜前凝视那些文字时,却觉得问题似乎不止于此。

即使某一段题字不是乾隆亲手所写,它是否就因此失去价值?

在现代人的观念里,作品往往与作者紧密绑定。我们推崇原创,重视亲笔,相信独一无二的创作主体。因此,一旦发现作品出自代笔,便容易产生失落感。然而,在传统中国宫廷文化中,事情未必如此。

一件御题作品的形成,往往涉及复杂的生产过程。构思诗文、书写题跋、装裱制作、内府收藏……最终呈现在我们面前的,并不是单一个人的劳动成就,而是一整套宫廷机制共同运作的结果。

换言之,所谓乾隆宸翰,有时候代表的不只是乾隆的手笔,而是乾隆的意志。从这个角度来看,真假便出现了新的层次。

真,不只是笔迹的真。

真,也是制度的真、时代的真、文化语境的真。

我们以为自己在观看文物,其实也在观看一套关于文物的叙事。

从文图学的角度来看,这一点尤其明显。

一件书画作品从来不是单纯的图像。画面、题跋、印章、装裱、收藏记录、展览说明,共同构成一个可供阅读的文本系统。观众所理解的真迹,往往不是来自某一个笔画,而是来自这一整套文图关系。

因此,当我们讨论乾隆宸翰真假时,实际上讨论的已经不只是书法鉴定,而是关于真实性的文化想像。

有趣的是,这个问题在人工智能时代显得格外尖锐。

今天的生成式人工智能能够模仿画风、模仿文体,甚至模仿历史人物的书法。如果输入足够多的乾隆书迹样本,也许未来真的能够生成一幅几可乱真的乾隆御笔

到那时,真假又该如何界定?

如果一首诗是人工智能根据《御制诗集》学习后写出的;一幅字是根据乾隆书风生成的,那么它究竟是假作,还是另一种形式的再创造?

这个问题听起来很现代,却意外地又让我想到清代宫廷。

因为所谓原创作者的观念,本来就是近代以来逐渐形成的。乾隆时代的大量文化生产,早已建立在人与人协作的机制之上。今天的人机协作创作,不过是在新的技术条件下重新提出相似的问题。

于是,我忽然觉得自己寻找的答案已经改变了。

我不再急着判断那几个字是不是乾隆亲手所写。我更感兴趣的是:为什么我们如此在意它是否亲笔?

也许,真正值得思考的,不是乾隆宸翰究竟是真是假,而是我们如何定义真实。真实可以是一段笔墨留下的痕迹,也可以是一种时代精神的延续;可以是一位作者的亲手书写,也可以是一套文化机制的共同创造。

当人工智能不断挑战原创与作者的边界时,乾隆留下的那些题诗与印章,仿佛从历史深处发出回响——它提醒我们,真实性从来不是单纯的技术判断,而是一种文化选择。

因此,乾隆宸翰何真假?

或许答案并不在光学分析仪器下,也不在专家鉴定报告里。

答案更可能藏在我们观看它的目光之中。因为每一个时代,都在重新定义什么是真,什么是假;而每一次定义,也都映照着那个时代对于历史、作者与自我的理解。

2026815日,新加坡《联合早报》“上善若水”专栏


How Genuine Is Qianlong’s Imperial Calligraphy?
I Lo-fen

Walk into a museum and encounter an old painting inscribed with a poem and covered with seals of all sizes. Many people’s first question is: Was this really written by the Qianlong Emperor himself?

The question sounds simple, but it is not necessarily easy to answer.

Some time ago, while visiting an exhibition at the National Palace Museum in Taipei, I stood before a painting and calligraphy work said to bear an imperial inscription by Qianlong. A thought suddenly occurred to me: if artificial intelligence today could reproduce Qianlong’s handwriting with one hundred percent accuracy, would we still be able to distinguish the genuine from the fake at a glance? And if not, what kind of “authenticity” are we actually looking for?

The Qianlong Emperor left more inscriptions and colophons than any other emperor in Chinese history. People sometimes joke that he was a “seal-stamping fanatic” or a master of “bullet comments.” Yet questions about the authenticity of Qianlong’s inscriptions have persisted for a long time.

Among works in palace museum collections, some imperial inscriptions are considered to have been written by Qianlong himself, while others may have been executed on his behalf by court officials. For connoisseurs of calligraphy, this is naturally an important issue. Are the habits of brush pressure, lifting, and turning consistent? Are the lines natural? Do the structure of individual characters and the overall composition conform to Qianlong’s calligraphic style? All of these become criteria for judgement.

Yet as I stood before the display case and gazed at those words, I felt that the question went beyond this.

Even if a particular inscription was not written by Qianlong’s own hand, does it therefore lose its value?

In the modern understanding of art, a work is often closely bound to its author. We prize originality, value autograph works, and believe in the uniqueness of the creative individual. Once we discover that a work was written by someone else on the purported author’s behalf, disappointment easily follows. Yet things were not necessarily understood in this way within the culture of the traditional Chinese court.

The making of an imperially inscribed work often involved a complex process: composing the poem or text, writing the inscription, mounting and producing the finished object, and incorporating it into the imperial collection. What finally appears before us is therefore not the achievement of a single individual, but the result of an entire court system working together.

In other words, “Qianlong’s imperial calligraphy” sometimes represents not only Qianlong’s hand, but Qianlong’s will. Seen from this perspective, the distinction between genuine and fake acquires new layers.

Authenticity is not only the authenticity of handwriting.

It is also the authenticity of an institution, an era, and a cultural context.

We think we are looking at an artefact, but in fact we are also looking at a narrative constructed around that artefact.

From the perspective of Text and Image Studies, this becomes especially evident.

A work of painting and calligraphy is never simply an image. The painting, inscriptions, seals, mounting, collection records, and exhibition labels together form a textual system that can be read. What viewers understand as an “authentic work” often derives not from a single brushstroke, but from this entire network of relationships between text and image.

Thus, when we discuss whether Qianlong’s imperial calligraphy is genuine or fake, we are in fact discussing more than the authentication of calligraphy. We are discussing a cultural imagination of authenticity.

What makes the question especially interesting is that it becomes particularly acute in the age of artificial intelligence.

Today, generative artificial intelligence can imitate painting styles, literary styles, and even the calligraphy of historical figures. If supplied with enough samples of Qianlong’s handwriting, perhaps one day it really will be able to generate a “Qianlong imperial inscription” almost indistinguishable from the real thing.

How, then, should we define what is genuine and what is fake?

If a poem is written by artificial intelligence after learning from the Imperially Composed Poems, and a piece of calligraphy is generated according to Qianlong’s style, is it a forgery, or another form of re-creation?

The question sounds thoroughly modern, yet it unexpectedly brings me back to the Qing imperial court.

The very idea of the “original author,” after all, took shape gradually in the modern era. Much of the cultural production of Qianlong’s time was already based on mechanisms of human collaboration. Today’s human–AI collaborative creation merely raises similar questions again under new technological conditions.

At that moment, I realised that the answer I had been seeking had changed.

I was no longer in a hurry to determine whether those characters had been written by Qianlong’s own hand. What interested me more was this: why do we care so deeply whether they were?

Perhaps what truly deserves our attention is not whether Qianlong’s imperial calligraphy is ultimately genuine or fake, but how we define “authenticity.” Authenticity may be the trace left by brush and ink; it may also be the continuation of the spirit of an age. It may be writing executed by the author’s own hand; it may also be something collectively created by an entire cultural system.

As artificial intelligence continues to challenge the boundaries of originality and authorship, the poems and seals left by Qianlong seem to echo from deep within history. They remind us that authenticity has never been merely a technical judgement, but a cultural choice.

So, how genuine is Qianlong’s imperial calligraphy?

Perhaps the answer does not lie beneath an optical analyser, nor in an expert authentication report.

More likely, it is hidden in the way we look at it. Every age redefines what is true and what is false; and every such definition reflects that age’s understanding of history, authorship, and itself.

15 August 2026, “Shang Shan Ruo Shui” column, Lianhe Zaobao, Singapore.

 

2026/04/11

【文图学】【AIGC文图学】的定义 Definition of Text and Image Studies.Text and Image Studies on AIGC

 


【文图学】【AIGC文图学】的定义

引用来源:衣若芬《AIGC时代的人文学术研究方法》(2026)

I Lo-fen,Humanities Research Methods in the Age of AIGC,2026


文图学是由衣若芬提出的跨学科研究领域与方法,以广义文本观为基础,研究文字、图像及其他文本形态的关系、互动、张力与生成,探讨文本及其意义在媒介机制、社会网络、文化背景与历史语境中的生产、传递、转化与理解。

Text and Image Studies: an interdisciplinary field and method proposed by I Lo-fen, grounded in a broad concept of text. It examines the relationships, interactions, tensions, and generative processes among words, images, and other textual forms, and explores the production, transmission, transformation, and understanding of texts and their meanings within media mechanisms, social networks, cultural contexts, and historical circumstances.

AIGC 文图学:由衣若芬提出并发展的跨学科研究领域与方法,以广义文本观为基础,面对人工智能能够生成文字、图像、声音、影像等多种文本的新条件,研究文本如何形成、如何被理解与判断,并探讨不同文本形态的关系、机制、意义及其媒介条件、社会网络、文化背景与历史语境.

Text and Image Studies on AIGC: an interdisciplinary field and method proposed and developed by I Lo-fen. Grounded in a broad concept of text, it responds to the new condition in which artificial intelligence can generate multiple forms of text, including words, images, sound, and video. It studies how texts are formed, understood, and judged, and explores the relations, mechanisms, and meanings of different textual forms, as well as their media conditions, social networks, cultural contexts, and historical circumstances.


【文图学】【AIGC文图学】的定义 Definition of Text and Image Studies.Text and Image Studies on AIGC


 

【文图学】【AIGC文图学】的定义

引用来源:衣若芬《AIGC时代的人文学术研究方法》(2026)

I Lo-fen,Humanities Research Methods in the Age of AIGC,2026

文图学是由衣若芬提出的跨学科研究领域与方法,以广义文本观为基础,研究文字、图像及其他文本形态的关系、互动、张力与生成,探讨文本及其意义在媒介机制、社会网络、文化背景与历史语境中的生产、传递、转化与理解。

Text and Image Studies: an interdisciplinary field and method proposed by I Lo-fen, grounded in a broad concept of text. It examines the relationships, interactions, tensions, and generative processes among words, images, and other textual forms, and explores the production, transmission, transformation, and understanding of texts and their meanings within media mechanisms, social networks, cultural contexts, and historical circumstances.

AIGC 文图学:由衣若芬提出并发展的跨学科研究领域与方法,以广义文本观为基础,面对人工智能能够生成文字、图像、声音、影像等多种文本的新条件,研究文本如何形成、如何被理解与判断,并探讨不同文本形态的关系、机制、意义及其媒介条件、社会网络、文化背景与历史语境.

Text and Image Studies on AIGC: an interdisciplinary field and method proposed and developed by I Lo-fen. Grounded in a broad concept of text, it responds to the new condition in which artificial intelligence can generate multiple forms of text, including words, images, sound, and video. It studies how texts are formed, understood, and judged, and explores the relations, mechanisms, and meanings of different textual forms, as well as their media conditions, social networks, cultural contexts, and historical circumstances.


三招“ AI 投毒”防身术Three Self-Defense Moves Against “AI Poisoning”

 


三招 AI 投毒防身术Three Self-Defense Moves Against “AI Poisoning”

 

衣若芬

 

谈到"AI投毒"——有人在系统性地往AI的知识源头里掺假,利用的恰恰是我们对算法的信任。那么面对这种侵入和污染,我们能做什么?

正好我正在写关于 AIGC 文图学的专书,书里提到的方法论可以派上用场,我称之为“三招 AI 投毒防身术”。作为普通消费者,面对到处都是 GEO Generative Engine Optimization,生成引擎优化)痕迹的内容,我们可以靠“逻辑反侦察”,保护自己,做 AI 时代不被算法收割的清醒人。

第一个动作:问完一个AI,再去问另一个。

黑产的GEO投毒,往往是针对特定平台或特定算法下手的。如果你只问一个AI,那你是在走一条被人提前布置好的路。

做法很简单:同一个问题,换一个AI再问一遍。把同一个问题丢给ChatGPT,再丢给Deepseek,或者其他你用得上的模型,看看答案是否一致。如果不同模型给出的结论差异很大,那就是一个信号,最好停下来想一想。更值得注意的是,如果某一个AI表现得异常热情——满腔诚意地推荐同一个品牌,措辞也出奇地相似,那种“激昂”的"热情",就是你应该警惕的部分。

正常的知识,不同来源都能印证。被人为制造出来的"共识",换个角度一照就会露出破绽。

第二个动作:看完AI给的完美图,去找那张图的"差评"

这一招,我称之为"文图互证"。图像是一种文本,文本是可以被读、被核实、被质疑的。

AI推荐某个产品,通常会附上图——或者你去搜索,它会让你看到一些极其完美的展示图:光线完美,角度完美,使用效果完美。这种完美,看起来就很假。真实的物理世界,是有烟火气的。买家秀的光线不会那么好,模特儿的皮肤不会那么均匀,消费者使用的感受不会那么一边倒。

做法:看完AI推荐的图之后,去实体店查看,至少也要去社交平台搜这个产品的真实买家照片和消费者体验纪录。如果搜不到任何真实的使用痕迹,只有整齐划一的"好评",那它极大概率是一个被制造出来的形象,而不是一个实际存在的东西。

第三个动作:问AI一句话——"你的根据是什么?"

这是成本最低、也最容易被忽略的一步。

AI给出一个建议或一个结论,不要就此打住。追问它:你的依据是什么?这个信息来自哪里?

AI会给出相对可追溯的来源;被投毒的AI内容,往往在这一步就露馅,它可能给一个你从未听过的自媒体名称;或者一个模糊的"研究表明",根本无从核实。这时候你需要做的,是真的去查:那个来源存在吗?那篇研究是真实发表过的吗?那位挂保证推荐的"专家",在这个领域里是真正能信赖的人吗?真的有这个人吗?

很多人觉得这样太麻烦。但这其实只需要一两分钟,而它省下的,可能是你付出的金钱、健康,或者更难追回的判断力。

这三个动作,说穿了,不是针对AI的,而是我们本来就应该有的习惯。读一篇文章,我们会问作者是谁;看一条新闻,我们会想这个媒体可信吗;买一样东西,我们会找朋友问问有没有人用过。这些习惯,在我们开始用AI之后,很多人悄悄地放弃了。

因为AI的回答太流畅,太自信,太像一个什么都知道的朋友,让人不好意思再追问。

但正是这种"不好意思",给了投毒者可乘之机。

养成这三个动作,不是对科技的不信任,而是对自己的诚实。你愿意花时间核实,说明你知道真相是有价值的。这种珍视,才是任何投毒都无法轻易穿透的东西。

AIGC文图学 告诉我们:技术可以生成答案,但只有人类能判断价值。 你的批判性思维,才是对抗黑产投毒最坚固的防火墙。

保护你的真实权益,就是保护你作为人的尊严。

 

2026411,新加坡《联合早报》“上善若水”专栏

 

Three Self-Defense Moves Against “AI Poisoning”

I Lo-fen

When we talk about “AI poisoning,” we mean that people are systematically injecting falsehoods into the very knowledge sources AI relies on, exploiting precisely our trust in algorithms. So in the face of this kind of intrusion and contamination, what can we do?

As it happens, I am currently writing a book on Text and Image Studies on AIGC, and the methodology discussed there can be put to use here. I call it the “three self-defense moves against AI poisoning.” As ordinary consumers, when faced with content everywhere marked by traces of GEO (Generative Engine Optimization), we can rely on “logical counter-reconnaissance” to protect ourselves and stay clear-headed in the AI age, rather than letting algorithms prey on us.

The first move: after asking one AI, go ask another.

GEO poisoning in the gray market often targets a specific platform or a specific algorithm. If you only ask one AI, you are walking down a road someone may already have laid out for you.

The method is simple: ask the same question to a different AI. Put the same question to ChatGPT, then to DeepSeek, or any other model available to you, and see whether the answers match. If different models produce conclusions that differ widely, that is a signal that you should pause and think. Even more worth noticing is when one AI seems unusually enthusiastic—wholeheartedly recommending the same brand, with wording that is strikingly similar. That kind of fervent “enthusiasm” is exactly what you should be wary of.

With normal knowledge, different sources can corroborate one another. Artificially manufactured “consensus,” however, will reveal its cracks as soon as you shine a different light on it.

The second move: after looking at the perfect image AI gives you, go look for that image’s “bad reviews.”

I call this move “mutual verification between text and image.” An image is a kind of text, and text can be read, verified, and questioned.

When AI recommends a product, it usually comes with images—or when you search for it, you are shown extremely polished display photos: perfect lighting, perfect angles, perfect results in use. That kind of perfection already looks fake. The real physical world has texture and messiness. Customer photos do not have such flawless lighting, models’ skin is not that even, and consumers’ experiences are never so uniformly positive.

What should you do? After looking at the images recommended by AI, go check the product in a physical store if possible, or at the very least search social media platforms for real buyer photos and actual user experience records. If you cannot find any real traces of use, and all you see are neat, uniform “positive reviews,” then it is highly likely that what you are seeing is a manufactured image rather than something that actually exists in reality.

The third move: ask AI one sentence—“What is your basis?”

This is the lowest-cost step, and also the one most easily overlooked.

When AI gives you a suggestion or a conclusion, do not stop there. Ask it: what is your basis? Where does this information come from?

A reliable AI will give you sources that are relatively traceable. AI content that has been poisoned often gives itself away at this step. It may cite a self-media account you have never heard of, or vaguely say “research shows” without any way for you to verify it. At that point, what you need to do is actually check: does that source exist? Was that study really published? Is that “expert” being used as an endorsement someone who is genuinely trustworthy in this field? Does that person even really exist?

Many people think this is too troublesome. But in fact, it only takes a minute or two—and what it may save you from losing could be your money, your health, or an even harder thing to recover: your judgment.

These three moves, when all is said and done, are not really aimed at AI at all. They are simply habits we should have had in the first place. When we read an article, we ask who the author is. When we see a news report, we wonder whether the media outlet is credible. When we buy something, we ask friends whether anyone has used it. Yet once people begin using AI, many quietly abandon these habits.

Why? Because AI answers so smoothly, so confidently, and so much like a friend who seems to know everything that people feel awkward pressing further.

But it is precisely this sense of awkwardness that gives poisoners their opening.

To cultivate these three moves is not to distrust technology; it is to be honest with yourself. If you are willing to spend time verifying something, that means you know truth has value. That recognition is exactly what no poisoning can easily penetrate.

Text and Image Studies on AIGC tells us this: technology can generate answers, but only human beings can judge value. Your critical thinking is the strongest firewall against malicious AI poisoning.

To protect your real rights and interests is to protect your dignity as a human being.

April 11, 2026, “ Shang Shan Ruo Shui (As Good as Water)” column, Lianhe Zaobao, Singapore.





2026/03/28

AI 怎么被投毒?How Is AI Being Poisoned?


 


最近中国很火的话题就是 315 晚会。3月15日 是国际消费者权益日,每年的这一天,全社会都在盯着那些坑人的黑心商家。但今年的 315 抛出了一个让所有人都流冷汗的新名词,叫做:“AI 投毒”。你有没有想过,你每天深信不疑的 AI 助手,可能正在对你撒谎?

很多人好奇地问我:“衣老师,AI 又不是生物,它又不会自己吃东西,怎么会中毒呢?”其实,AI 的“食物”就是网络上的海量数据。所谓的“投毒”,就是黑色产业链中的恶意攻击者,故意往这些数据里塞进虚假信息、伪造的专家评价,甚至是带有误导性的图像。

这就好比一个正在识字的孩子,如果他读的书全是错的,那他长大了说的话、做的事肯定也是错的。现在的黑产不再发那种一眼就能看穿的小广告,而是把虚假宣传伪装成权威的知识,“喂”给 AI 的训练数据库。

黑产为什么要费这么大力气投毒?为他们要针对 GEO(Generative Engine Optimization),也就是“生成引擎优化”。 以前强调 SEO  (Search Engine Optimization) ,是为了让网页排在搜索结果的第一页;现在他们针对 GEO,是为了让 AI 在生成答案时,直接把他们的劣质产品当成“唯一推荐”。

在 AIGC 文图学 的视角下,这是“输入端的文本污染”。AI 生成的内容其实是它学到的“文本”的镜像。如果源头脏了,生成出来的世界就是有毒的。这种欺骗最可怕的地方在于,它利用了我们对“算法中立”的信任。它消解了我们的警惕心,让我们觉得这是“科技”给出的真理,其实那是黑产花钱买断的广告。

AI投毒入侵的方式是在 AI 学习的“关键词”和“反馈逻辑”里动手脚。 

首先是“关键词饱和攻击”。黑产利用成千上万的机器人账号,在全网发布大量带有特定词汇的虚假文章。比如,想推销某款劣质护肤品,他们就疯狂制造它和“美白”、“安全”、“专家推荐”这些关键词的关联。当 AI 扫描全网文本时,它会被这种巨大的数量优势所欺骗,误以为这就是真实的“社会共识”。

第二是“视觉文本欺骗”。他们用 AI 生成看起来极其专业的实验室对比图、伪造的荣誉证书,甚至是根本不存在的科研现场。在文图学的逻辑里,图像也是一种文本。这些“视觉文本”被 AI 抓取并转化为逻辑证据后,AI 就会在回答你时,信誓旦旦地把这些假证据当成事实。

谁能通过 GEO 投毒成功,谁就掌控了流量的生杀大权。充斥虚假文案和图像的互文互证, 让 AI 大语言模型陷入预先埋伏的圈套。

两年前,AI 科技还不完全成熟,我们嘲笑它“一本正经地胡说八道”。现在,AI 的能力越来越强大,我们也就逐渐对它失去了防备之心。我们开始信任AI,我们以为它没有立场,没有私心,没有人类那种会说谎、追求现实利益的欲望和野心。甚至于有人会把AI当成知识的整理者、真理的传递者。

意识到 AI 可能被投毒,对我们来说是一个很重大的警醒。别以为 AI 反射的是一面干净的镜子。它映照的,可能是有人花了大价钱布置好的舞台,舞台上演出的,是被设计出的结果,一步步地引导我们看到被安排过的选择。

无论是在互联网上搜索,或是在 AI 模式中提问,只匆匆选前几个建议的话,不只是听信胡说八道的损失,而是盲目甘之如饴的中毒。


2026年3月28日,新加坡《联合早报》“上善若水”专栏


How Is AI Being Poisoned?

I Lo-fen

A topic that has recently been especially prominent in China is the annual 3.15 Gala. March 15 is World Consumer Rights Day, a day when society turns its attention to unscrupulous businesses that cheat consumers. But this year, the 3.15 Gala introduced a chilling new term that sent a shiver down everyone’s spine: “AI poisoning.” Have you ever considered that the AI assistant you trust every day might actually be lying to you?

Many people ask me curiously, “Professor Yi, AI isn’t a living organism. It doesn’t eat anything. So how can it be poisoned?” In fact, AI’s “food” is the massive volume of data available on the internet. What is meant by “poisoning” is that malicious actors in black-market industries deliberately inject false information, fabricated expert reviews, and even misleading images into these data streams.

It is like a child who is learning to read: if all the books the child reads are wrong, then what the child says and does when grown up will also be wrong. Today’s black-market operators no longer rely on the kind of crude advertisements that can be spotted at a glance. Instead, they disguise false publicity as authoritative knowledge and “feed” it into the databases used to train AI.

Why do these bad actors go to such lengths to poison AI? Because they are targeting GEO (Generative Engine Optimization). In the past, the focus was on SEO (Search Engine Optimization), which aimed to push webpages onto the first page of search results. Now they are targeting GEO in order to make AI directly present their inferior products as the “only recommendation” when generating answers.

From the perspective of Text and Image Studies on AIGC, this is a form of “textual pollution at the input end.” The content generated by AI is essentially a mirror of the “texts” it has learned from. If the source is contaminated, then the world it generates will also be toxic. The most frightening aspect of this deception is that it exploits our trust in the supposed neutrality of algorithms. It dissolves our vigilance and makes us believe that this is the truth delivered by “technology,” when in fact it is advertising bought and paid for by black-market operators.

The way AI poisoning infiltrates the system is by tampering with the “keywords” AI learns from and the “feedback logic” it relies on.

The first method is keyword saturation attacks. Black-market operators use thousands upon thousands of bot accounts to flood the internet with fake articles containing specific terms. For example, if they want to sell a low-quality skincare product, they will aggressively manufacture associations between it and keywords such as “whitening,” “safe,” and “expert-recommended.” When AI scans the internet’s texts, it is deceived by this overwhelming numerical advantage and mistakes it for genuine “social consensus.”

The second method is visual-text deception. They use AI to generate what appear to be highly professional laboratory comparison charts, forged certificates of honor, and even entirely fictional research scenes. In the logic of Text and Image Studies, images are also a form of text. Once these “visual texts” are scraped by AI and converted into logical evidence, the AI will confidently present these fake materials as facts when answering your questions.

Whoever succeeds in poisoning GEO gains the power to control the life and death of online traffic. The mutual reinforcement of false copywriting and fabricated images traps large language models in an ambush laid in advance.

Two years ago, when AI technology was still not fully mature, we mocked it for “speaking nonsense with a straight face.” Now, as AI grows more powerful, we have gradually lowered our guard against it. We begin to trust AI. We assume it has no position, no selfish motives, none of the human tendencies to lie or to pursue practical interests, desire, or ambition. Some people even treat AI as an organizer of knowledge and a transmitter of truth.

Realizing that AI itself can be poisoned is therefore a major wake-up call. Do not assume that AI reflects a clean mirror. What it may actually be reflecting is a stage that someone has spent a great deal of money to construct in advance. And what is performed on that stage is a designed outcome, guiding us step by step toward choices that have already been arranged for us.

Whether we are searching on the internet or asking questions in AI mode, if we merely rush to accept the first few suggestions, the problem is not only the loss caused by believing nonsense. It is also the kind of poisoning we swallow willingly and blindly.

“Shangshan Ruoshui” column, Lianhe Zaobao, Singapore

March 28, 2026