How The ChatGPT Watermark Works And Why It Might Be Defeated

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OpenAI’s ChatGPT introduced a way to automatically create content but plans to present a watermarking function to make it easy to identify are making some individuals worried. This is how ChatGPT watermarking works and why there might be a way to defeat it.

ChatGPT is an extraordinary tool that online publishers, affiliates and SEOs all at once enjoy and dread.

Some online marketers like it since they’re discovering new ways to use it to produce material briefs, describes and complicated articles.

Online publishers hesitate of the prospect of AI material flooding the search results page, supplanting professional posts written by people.

As a result, news of a watermarking function that opens detection of ChatGPT-authored material is also expected with anxiety and hope.

Cryptographic Watermark

A watermark is a semi-transparent mark (a logo design or text) that is embedded onto an image. The watermark signals who is the initial author of the work.

It’s largely seen in photos and increasingly in videos.

Watermarking text in ChatGPT involves cryptography in the kind of embedding a pattern of words, letters and punctiation in the form of a secret code.

Scott Aaronson and ChatGPT Watermarking

An influential computer system scientist named Scott Aaronson was hired by OpenAI in June 2022 to work on AI Safety and Alignment.

AI Security is a research study field worried about studying ways that AI may position a damage to human beings and developing ways to avoid that type of unfavorable interruption.

The Distill scientific journal, including authors affiliated with OpenAI, defines AI Security like this:

“The objective of long-term expert system (AI) security is to guarantee that innovative AI systems are dependably lined up with human worths– that they dependably do things that people want them to do.”

AI Positioning is the expert system field interested in ensuring that the AI is lined up with the desired goals.

A big language model (LLM) like ChatGPT can be utilized in such a way that may go contrary to the objectives of AI Alignment as defined by OpenAI, which is to create AI that advantages mankind.

Accordingly, the factor for watermarking is to avoid the misuse of AI in a way that damages mankind.

Aaronson discussed the reason for watermarking ChatGPT output:

“This could be handy for preventing academic plagiarism, obviously, however also, for instance, mass generation of propaganda …”

How Does ChatGPT Watermarking Work?

ChatGPT watermarking is a system that embeds a statistical pattern, a code, into the options of words and even punctuation marks.

Content created by expert system is generated with a fairly foreseeable pattern of word option.

The words composed by human beings and AI follow a statistical pattern.

Changing the pattern of the words used in produced material is a method to “watermark” the text to make it simple for a system to spot if it was the product of an AI text generator.

The trick that makes AI content watermarking undetected is that the distribution of words still have a random appearance comparable to normal AI created text.

This is referred to as a pseudorandom distribution of words.

Pseudorandomness is a statistically random series of words or numbers that are not actually random.

ChatGPT watermarking is not presently in usage. However Scott Aaronson at OpenAI is on record specifying that it is prepared.

Today ChatGPT is in previews, which enables OpenAI to discover “misalignment” through real-world usage.

Presumably watermarking might be introduced in a last version of ChatGPT or quicker than that.

Scott Aaronson blogged about how watermarking works:

“My primary task up until now has been a tool for statistically watermarking the outputs of a text model like GPT.

Essentially, whenever GPT creates some long text, we desire there to be an otherwise undetectable secret signal in its options of words, which you can use to show later on that, yes, this came from GPT.”

Aaronson discussed further how ChatGPT watermarking works. However initially, it’s important to comprehend the idea of tokenization.

Tokenization is an action that happens in natural language processing where the device takes the words in a file and breaks them down into semantic systems like words and sentences.

Tokenization changes text into a structured form that can be utilized in machine learning.

The process of text generation is the machine guessing which token comes next based upon the previous token.

This is finished with a mathematical function that identifies the probability of what the next token will be, what’s called a probability distribution.

What word is next is forecasted however it’s random.

The watermarking itself is what Aaron describes as pseudorandom, because there’s a mathematical factor for a specific word or punctuation mark to be there but it is still statistically random.

Here is the technical description of GPT watermarking:

“For GPT, every input and output is a string of tokens, which might be words but likewise punctuation marks, parts of words, or more– there have to do with 100,000 tokens in overall.

At its core, GPT is constantly creating a likelihood circulation over the next token to produce, conditional on the string of previous tokens.

After the neural net creates the distribution, the OpenAI server then really samples a token according to that circulation– or some modified version of the distribution, depending upon a specification called ‘temperature.’

As long as the temperature level is nonzero, though, there will generally be some randomness in the option of the next token: you might run over and over with the exact same prompt, and get a different conclusion (i.e., string of output tokens) each time.

So then to watermark, instead of choosing the next token arbitrarily, the idea will be to pick it pseudorandomly, utilizing a cryptographic pseudorandom function, whose secret is understood just to OpenAI.”

The watermark looks entirely natural to those reading the text due to the fact that the choice of words is simulating the randomness of all the other words.

But that randomness includes a predisposition that can only be found by someone with the key to decipher it.

This is the technical description:

“To highlight, in the diplomatic immunity that GPT had a lot of possible tokens that it judged equally probable, you could merely choose whichever token taken full advantage of g. The choice would look uniformly random to somebody who didn’t understand the secret, but someone who did know the secret could later sum g over all n-grams and see that it was anomalously big.”

Watermarking is a Privacy-first Option

I’ve seen discussions on social networks where some individuals recommended that OpenAI might keep a record of every output it produces and use that for detection.

Scott Aaronson verifies that OpenAI could do that but that doing so postures a privacy issue. The possible exception is for police scenario, which he didn’t elaborate on.

How to Spot ChatGPT or GPT Watermarking

Something intriguing that seems to not be well known yet is that Scott Aaronson kept in mind that there is a method to beat the watermarking.

He didn’t say it’s possible to beat the watermarking, he stated that it can be defeated.

“Now, this can all be defeated with adequate effort.

For instance, if you utilized another AI to paraphrase GPT’s output– well all right, we’re not going to be able to spot that.”

It appears like the watermarking can be defeated, at least in from November when the above statements were made.

There is no sign that the watermarking is presently in use. However when it does enter usage, it may be unidentified if this loophole was closed.


Check out Scott Aaronson’s post here.

Featured image by SMM Panel/RealPeopleStudio