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Claude文本生成引入隐形水印技术

Claude Is Now Leaving Invisible Fingerprints In Its Text

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Claude直接部署文本水印是AI安全与治理的重要进展,直接影响内容溯源与合规,值得从业者关注其技术原理与实际影响。

Images can be watermarked for copy protection. Now get this. Claude AI announced that they are watermarking the text you generate with it. From when? When does this start? Well, Anthropic is rolling this out right now. Yep, I am not talking about this because I agree with it, but because I think it's important that all of you fellow scholars know about this to inform the public. So, this piece of text is watermarked.

图片可以添加水印以进行版权保护。现在听好了,Claude AI 宣布他们会对你用其生成的文本进行水印处理。从什么时候开始?何时启动?嗯,Anthropic 正在此刻推出这项功能。是的,我并非因为赞同这一做法而谈论它,而是我认为所有同僚学者都应该了解此事,以便向公众通报。因此,这段文字已被添加了水印。

Wait, what?

等等,什么?

[laughter]

[笑声]

You can watermark an image by putting your logo on it. But text, how would you watermark text? You put hidden characters in it, right? Nope. This paper describes that it is a fingerprint in text that is invisible to humans but is detectable for machines. It even survives copy pasting and some editing too. I'll tell you what it doesn't survive in a minute. So when generating text, the AI decides what the next word should be.

你可以通过在图片上放置标志来为图片添加水印。但文本呢,该如何为文本添加水印?你在其中放入隐藏字符,对吧?不对。这篇论文描述了一种在文本中嵌入指纹的技术,它对人类不可见,但机器可以检测。它甚至能幸存于复制粘贴和一些编辑操作。我稍后会告诉你它无法幸存的情况。因此,当生成文本时,AI 会决定下一个词应该是什么。

And there can be a few candidates. Here you could say, I saw a dog, a puppy, a cat, or a house. Based on context, each of these words gets a probability to be chosen. Now, with a fingerprinting algorithm, it secretly assigns a color to each word. Some are green, preferred, some are red, not preferred. And now comes a little nepotism. Woohoo. A little cheating, if you will. When choosing the next word, the green ones get a little nudge upwards.

可能会有几个候选词。在这里你可以说“我看到了一只狗、一只小狗、一只猫或一所房子”。根据上下文,每个词被选中的概率不同。现在,通过一种指纹算法,它会秘密地为每个词分配颜色。有些是绿色(首选),有些是红色(非首选)。接下来有点徇私舞弊了。哇哦。如果愿意的话,可以说是一点点作弊。在选择下一个词时,绿色的词会得到一点向上的推动。

They will occur slightly more often. So, here's how to check for a watermark in a piece of text. Someone who knows the red and green words simply counts how many greens you have. This scheme has a mathematical property where as you see more and more green, the probability of it being real human text is extremely small. Found 21 greens in a paragraph. Suddenly the probability of that done by humans can be less than winning the lottery.

它们出现的频率会略高一些。那么,如何检查一段文本中是否存在水印呢?知道红绿词的人只需统计你有多少个绿词。这种方案具有一个数学特性:随着你看到的绿词越来越多,其为真实人类文本的概率就极小。如果在一段话中发现 21 个绿词,那么由人类完成这种情况的概率可能比中彩票还低。

Much smaller. Note that the green words can be anything no matter how inconspicuous. So you can't spot them. Which words are green can also change over time. Ouch. This is the simplified version of the algorithm. They are likely using the synth ID variant which has context dependent probabilities and a tournament system too. But the heart of the algorithm is the same in most research papers I read. Some words are preferred and are given a slight edge in the generation creating a unique fingerprint.

要小得多。请注意,绿词可以是任何词,无论多么不显眼。所以你无法察觉它们。哪些词是绿色的也会随时间变化。哎哟。这是该算法的简化版本。他们很可能使用的是 SynthID 变体,该变体也具有上下文依赖的概率和锦标赛系统。但我阅读的大多数研究论文中,算法的核心是相同的:某些词受到偏好,并在生成过程中获得轻微优势,从而形成独特的指纹。

Now, there are a lot of misconceptions about this out there. One, Claude written text cannot be traced back to you, but it shows that Claude wrote the whole thing or heavily edited the text. I don't agree with this. I am making this video to let everyone knowledge only changes the world when it reaches people. Second, some say just edit a few words and it's clean. Nope, you can't get rid of it so easily. So can you get rid of it?

现在,关于这一点存在许多误解。其一,Claude 生成的文本无法追溯到你,但这表明 Claude 撰写了全文或对文本进行了大量编辑。我不同意这种观点。我制作这个视频是为了让大家知道,只有当知识触达人们时,它才能改变世界。其二,有人说只需修改几个字就能清除痕迹。不行,你不可能这么轻易地摆脱它。那么,你能摆脱它吗?

Dear fellow scholars, this is two minute papers with Dr. Koa Eher. With light editing, no. If you rewrite the whole thing, exchanging every word, yes, you can get rid of it. An open weights LLM that works for you can also help. Okay. So, who can check if there is a watermark in the text? Well, not you and not me. Some eligible organizations can, but that's it for now. So, what is the solution? Well, of course, use free and open weights AI systems and run them yourself.

亲爱的学者们,这里是《两分钟论文》,由 Koa Eher 博士带来。经过轻微编辑,不行。如果你重写整个内容,替换每一个词,是的,你可以摆脱它。一个为你服务的开源权重大语言模型(LLM)也能提供帮助。好的。那么,谁能检查文本中是否有水印呢?嗯,不是你,也不是我。一些符合条件的机构可以,但目前就这些。那么,解决方案是什么?当然,使用免费且开源权重的 AI 系统,并自行运行它们。

These work for you, not against you. [music] That is the way of the scholar. We need new tools for the era of LLMs and Weights and Biases now has weave, a lightweight toolkit to confidently iterate on LLM applications. Use traces to debug how data flows through each step of your app and use evaluations to measure your progress. It is the best. Try it out now at wnb.me/papers me/papers or click the link in the description below.

这些工具是为你服务的,而不是与你作对。[音乐] 这就是学者的道路。我们需要适用于大语言模型时代的新工具,而 Weights and Biases 现在推出了 weave,这是一个轻量级工具包,可让你自信地对 LLM 应用进行迭代。使用追踪功能来调试数据如何在应用的每一步中流动,并使用评估功能来衡量你的进展。这是最好的选择。立即在 wnb.me/papers me/papers 或点击下方描述中的链接试用。

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