HN 提问：Anthropic 的水印是否可能比我们想象的要简单得多？
1 分•作者: CITIZENDOT•大约 2 小时前
Anthropic 最近表示他们正在为 Claude 的输出开发水印技术，同时声称这不会影响生成质量。
我想知道这是否仅仅是哈希指纹识别。
例如，将生成的文本分割成重叠的块：
“公司报告了强劲的增长……”
“报告了强劲的收入增长……”
“第二季度收入强劲增长……”
……
对每个块进行哈希处理并存储这些哈希值。当提交文本进行检测时，执行相同的操作并计算数据库中已有的块哈希数量。
即使有人修改了几个词，许多重叠的块仍然可能匹配。
搜索本身并不是真正的问题。使用 256 位哈希，您处理的是一个 2^256 的空间，但您只搜索您实际存储的哈希。二进制搜索可以在 256 次迭代中搜索任何哈希。
这也能满足 Anthropic 的要求：*在令牌生成过程中无需更改任何内容*，因此没有质量权衡：https://x.com/i/status/2088343978873966687
显而易见的问题是，他们如何处理如此大规模的误报率。
这能解释他们的方法吗，还是我遗漏了什么？
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Anthropic recently said they're working on watermarking Claude output, while also saying it won't interfere with generation quality.<p>I'm wondering if is just hash-fingerprinting.<p>For example, take the generated text and split it into overlapping chunks:<p><pre><code> "The company reported strong growth..."
"reported strong growth in revenue..."
"strong growth in revenue during Q2..."
...
</code></pre>
Hash each chunk and store the hashes. When text is submitted for detection, do the same thing and count how many chunk hashes are already in the database.<p>Even if someone edits a few words, many overlapping chunks could still match.<p>The search itself isn't really a problem. With 256-bit hashes you're dealing with a 2^256 space, but you only search the hashes you've actually stored. Binary search would search any hash in 256 iterations.<p>This also satisfies the Anthropic requirements: *nothing needs to be changed during token generation*, so there's no quality tradeoff: https://x.com/i/status/2088343978873966687<p>The obvious question is how they handle false-positive rate works at their scale.<p>Could this explain their approach, or is there something I'm missing?