AI Watermark

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What is an AI Watermark?

An AI watermark is a special type of digital pattern that indicates digital content was machine-generated.

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Standardized AI watermarks are expected to play an important role in future responsible AI initiatives and help combat misinformation. The ideal AI watermark is hard to remove but easily detected with the human eye or the right software tool.

Why are AI Watermarks Important?

As generative AI and multimodal AI software continue to improve and become more accessible, it’s becoming harder for humans to distinguish between human-generated content and AI-generated content.

AI watermarks that clearly identify machine-generated content will help maintain the integrity of online information by allowing content moderators and content consumers to know when they are viewing or listening to content that was created by a computer program.

Watermarks for AI content are an important concern for governments, news sources, academic researchers, and creative industries where the content’s source can impact the content’s value.

How are AI Watermarks Created?

AI watermarks can be embedded in generative workflows or added post-production. Ideally, the watermark should serve its purpose without affecting the quality or usability of the AI-generated content.

Embedded AI watermarks are created with code snippets or markers that are written directly into generative algorithms. This type of watermark can be hard to detect without a special software tool because it ends up being woven into the generated content’s structure. Any attempt to remove or alter an embedded watermark will automatically change the generated output.

Post-processing AI watermarks are graphic overlays or snippets that are added at the end of the generative workflow. This type of watermark can often be viewed with the naked eye.

Because post-processing watermarks are not integrated into the generative process, however, they are easier to remove or modify without affecting the generated content.

Types of AI Watermarks

5 Types of AI Watermarks

Popular approaches to creating watermarks that identify AI-generated content include:

  • Overlay Watermarks: This post-processing technique superimposes a graphical watermark on top of AI-generated content. Overlay watermarks are usually designed to be seen by anyone viewing the content. Because they are not integrated into the content’s structure, overlay watermarks can often be removed.
  • Steganographic Watermarks: This embedded technique inserts subtle patterns into generated content to create a watermark. Steganographic AI watermarks, which may also be referred to as perturbation watermarks, are designed to be difficult for humans to detect without special software. For images or videos, this technique might involve making slight changes to certain pixel For audio, it might involve making a slight change to a specific acoustic signal. For text, steganographic watermarks might be created by adding additional spaces in certain places, using specific types of sentence structures, or consistently using passive voice.
  • Metadata Watermarks: This post-processing technique adds additional information to the generated content’s metadata. Typically, this type of watermark includes information about the AI model used, the date of creation, and the creator’s details. Metadata watermarks can be easily viewed by tech-savvy humans and removed or altered by editing the metadata file.
  • Cryptographic Watermarks: This advanced watermarking technique encrypts the watermark to make it harder to detect without the corresponding decryption Cryptographic watermarks can be embedded into the generation process or added post-processing. The good news is that cryptographic watermarks are difficult to remove. The bad news is that they can also be difficult to implement.
  • Blockchain Watermarks: This advanced post-processing technique works a lot like cryptographic watermarks, except it uses hash functions instead of cryptography keys, and the hash is stored on a blockchain. When someone wants to verify the origin of the content, they need to know what hash function was used to create the original hash. Then, they have to generate a hash of the content in question and compare it with the hash stored on the blockchain. If the hashes match, it confirms that the content is AI-generated.

Why Should AI Watermarks be Standardized?

Currently, there is no single standardized format or approach for watermarking AI-generated images, videos, or text. Legally, it is up to the vendor to decide whether to watermark AI content, and they have the power to decide how it should be done.

In 2023, Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI publicly committed to creating watermarks that will allow users to know when content is AI-generated but implementing this particular AI guardrail is proving to be easier said than done.

Company Approach
Amazon Amazon said that images generated by Amazon Titan will contain an invisible watermark.
Anthropic In some specific contexts, the generated text could potentially be watermarked with hash functions.
Google DeepMind Has a new watermark app in Beta that can embed a digital watermark directly into image pixels.
Meta Has created a watermark for generated images that can be detected by algorithms even if someone edits the image.
Microsoft They will use cryptographic methods to add a digital watermark to all AI-generated images in Bing.
OpenAI Allows users to remove watermarks from content generated by their popular image generator, which is called DALL-E.
Adobe Adobe is pushing the idea of a very visible AI watermark that points to metadata about an image’s origin.

Regulatory compliance is likely to shape the future of generative AI and eventually standardize the way AI-generated content is labeled.

Governments around the world are becoming increasingly concerned about the potential misuse of AI-generated content, and many countries are exploring the idea of mandating digital watermarks for AI-generated content to prevent deepfakes from being used to spread misinformation and manipulate public opinion.

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Margaret Rouse
Editor

Margaret jest nagradzaną technical writerką, nauczycielką i wykładowczynią. Jest znana z tego, że potrafi w prostych słowach pzybliżyć złożone pojęcia techniczne słuchaczom ze świata biznesu. Od dwudziestu lat jej definicje pojęć z dziedziny IT są publikowane przez Que w encyklopedii terminów technologicznych, a także cytowane w artykułach ukazujących się w New York Times, w magazynie Time, USA Today, ZDNet, a także w magazynach PC i Discovery. Margaret dołączyła do zespołu Techopedii w roku 2011. Margaret lubi pomagać znaleźć wspólny język specjalistom ze świata biznesu i IT. W swojej pracy, jak sama mówi, buduje mosty między tymi dwiema domenami, w ten…