3 ms·
Great, but SynthID watermarking isn't a cryptographically-secure scheme, nor could one be made, because all the things necessary for the scheme to be crypto-sec
by kmeisthax 2mo ago
Great, but SynthID watermarking isn't a cryptographically-secure scheme, nor could one be made, because all the things necessary for the scheme to be crypto-secure are also things that go against being human-comprehensible.
The SynthID watermark scheme relies on the LLM's own logits being a map into acceptable substitutions for specific words. For example, if we have the sentence "The quick brown fox jumps over the lazy dog", the LLM might give the last word[0] as 98.5% "dog", 1% "hound", 0.5% "canine". In normal decoding we sample the logits after scaling them against a temperature parameter; in SynthID we additionally bias the logits to encode output from the watermark CSPRNG - e.g. the CSPRNG might decide to boost the third logit of this particular token to 65%.
But wait! The LLM might have selected valid synonyms for the token, but those synonyms will never be correct because this is a very well-known sentence. To humans, it's one sentence-long token, in the sense that most English-speaking humans who encounter this sentence will see the first few words and already know the ending. It thus has one bit of entropy[1]. Adding SynthID watermarks to this sentence necessarily has to change the words and thus adds more information - namely, that some process outside of the LLM itself has modified the text.
We don't need to cryptographically distinguish AES from random if both the AES (SynthID) and random (normal decoding) happens to also be constrained to form a valid English sentence.
[0] I am too lazy to account for multi-token words. Let's just assume the LLM was trained with a pretokenizer that splits exclusively on whitespace and every word in the training set got assigned a token.
[1] Compressible information
- usef- 2mo agoYour example is not realistic: it won't arbitrarily change a 0.5% token up to 65%. If it did it would absolutely degrade performance in a very measurable way. It's only biasing the randomness for tokens where it does have multiple plausible choices. For constrained quotes like "quick brown..." the synthid watermark would be weak, but that's by design.
- red_admiral 2mo agoThat doesn't match my understanding of how the watermark works. Let's say we have your distribution of (0.985, 0.01, 0.005). The AI somehow has to sample that distribution, let's say the pre-watermark model picks as many uniform (i.i.d.) bits as it needs to do the sampling, then does a Smirnov transform or something to get the correct distribution. The AI with watermark picks its bits from a CSPRNG, then does the same transform to get the distribution. The result I think is that you can reduce distinguishing the distribution to breaking the CSPRNG. In this example the entropy after the transform is around 0.13 bits, so the watermarking scheme cannot add any useful information. Once you have hundreds or prehaps thousands of bits of entropy, it starts to become feasible to add information into that 'spare' space.