How The ChatGPT Watermark Functions And Why It Could Be Defeated

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OpenAI’s ChatGPT introduced a method to automatically create content but prepares to present a watermarking feature to make it simple to find are making some individuals worried. This is how ChatGPT watermarking works and why there may be a way to defeat it.

ChatGPT is an extraordinary tool that online publishers, affiliates and SEOs at the same time love and dread.

Some online marketers enjoy it because they’re finding new ways to use it to produce material briefs, details and complicated articles.

Online publishers hesitate of the prospect of AI content flooding the search engine result, supplanting expert posts written by people.

Subsequently, news of a watermarking function that unlocks detection of ChatGPT-authored content is likewise prepared for with stress and anxiety and hope.

Cryptographic Watermark

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

It’s largely seen in photographs and significantly in videos.

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

Scott Aaronson and ChatGPT Watermarking

A prominent computer system scientist called Scott Aaronson was hired by OpenAI in June 2022 to deal with AI Safety and Alignment.

AI Safety is a research field concerned with studying manner ins which AI may present a harm to humans and developing methods to prevent that kind of unfavorable disruption.

The Distill scientific journal, including authors affiliated with OpenAI, specifies AI Safety like this:

“The objective of long-lasting artificial intelligence (AI) safety is to guarantee that innovative AI systems are reliably aligned with human values– that they dependably do things that individuals want them to do.”

AI Alignment is the artificial intelligence field concerned with making sure that the AI is aligned with the designated objectives.

A big language model (LLM) like ChatGPT can be utilized in a way that may go contrary to the objectives of AI Positioning as specified by OpenAI, which is to produce AI that benefits humankind.

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

Aaronson discussed the reason for watermarking ChatGPT output:

“This could be valuable for avoiding academic plagiarism, obviously, but likewise, for example, mass generation of propaganda …”

How Does ChatGPT Watermarking Work?

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

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

The words written by humans and AI follow an analytical pattern.

Altering the pattern of the words utilized in generated material is a way to “watermark” the text to make it easy for a system to identify if it was the product of an AI text generator.

The trick that makes AI content watermarking undetectable is that the circulation of words still have a random look comparable to regular AI created text.

This is referred to as a pseudorandom circulation of words.

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

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

Today ChatGPT remains in previews, which permits OpenAI to find “misalignment” through real-world use.

Probably watermarking may be presented in a last version of ChatGPT or faster than that.

Scott Aaronson discussed how watermarking works:

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

Essentially, whenever GPT produces some long text, we desire there to be an otherwise undetectable secret signal in its choices of words, which you can utilize to prove later that, yes, this came from GPT.”

Aaronson explained further how ChatGPT watermarking works. But first, it’s important to understand the concept of tokenization.

Tokenization is an action that occurs in natural language processing where the machine takes the words in a document and breaks them down into semantic units like words and sentences.

Tokenization changes text into a structured type that can be used in artificial intelligence.

The process of text generation is the device thinking which token comes next based on the previous token.

This is made with a mathematical function that determines the possibility 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 refers to as pseudorandom, in that there’s a mathematical factor for a particular word or punctuation mark to be there however 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 could be words but likewise punctuation marks, parts of words, or more– there are about 100,000 tokens in total.

At its core, GPT is constantly generating a probability distribution over the next token to produce, conditional on the string of previous tokens.

After the neural net produces the circulation, the OpenAI server then in fact samples a token according to that distribution– or some customized version of the distribution, depending on a criterion called ‘temperature level.’

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

So then to watermark, rather of picking the next token randomly, the idea will be to select it pseudorandomly, using a cryptographic pseudorandom function, whose key is understood just to OpenAI.”

The watermark looks completely natural to those reading the text since the option of words is mimicking the randomness of all the other words.

However that randomness includes a bias that can only be identified by somebody with the secret to decode it.

This is the technical description:

“To illustrate, in the special case that GPT had a lot of possible tokens that it judged equally probable, you might simply select whichever token optimized g. The choice would look consistently random to somebody who didn’t know the key, but somebody who did understand the key might later sum g over all n-grams and see that it was anomalously large.”

Watermarking is a Privacy-first Option

I have actually seen conversations on social networks where some individuals suggested that OpenAI might keep a record of every output it generates and utilize that for detection.

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

How to Spot ChatGPT or GPT Watermarking

Something intriguing that appears to not be popular yet is that Scott Aaronson kept in mind that there is a way to defeat the watermarking.

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

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

For example, if you used another AI to paraphrase GPT’s output– well alright, we’re not going to have the ability to spot that.”

It looks like the watermarking can be defeated, a minimum of in from November when the above statements were made.

There is no indicator that the watermarking is presently in usage. But when it does enter into use, it might be unidentified if this loophole was closed.


Check out Scott Aaronson’s article here.

Included image by SMM Panel/RealPeopleStudio