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A New Gold Standard for Watermarks

Introducing PawPrint

The first neural watermark designed for provenance at scale.
Updated July 1, 2026

We are introducing our new line of AI-powered watermarks, PawPrint, available for image and audio and video. PawPrint represents a massive leap in performance over existing models, across all dimensions (speed, accuracy, durability, perceptibility, etc.). It can be swiftly deployed in any business use case where digital media content needs to be reliably identified. PawPrint is available today as part of our C2PA Signing API & SDK services.

Overview

Trufo's pawprint series is the strongest watermark on the market, across image, audio, video.

pawprint.image
94
pixelseal
86
jpeg2026
80
competitor est.
80
trustmark.p
78
trustmark.q
76
vine
70
legacy est.
50
dctdwtsvd
42
pawprint.audio
96
audioseal
72
competitor est.
65
wavmark
62
silentcipher
62
audiowmark
62
perth
60
legacy est.
60
Full model report coming soon.

Durable Provenance

Trufo's pawprint watermarks are integrated into Trufo's provenance services. This includes C2PA soft-binding recovery, dedicated decoding endpoints, local watermarking SDKs, and more. If you would like to learn more, please visit the dashboard or schedule a consultation.

Video

Full video model report coming soon.

Frequently Asked Questions

You can use PawPrint through our API services. Please head over to our dashboard at app.trufo.ai to get started.
Yes, our provenance SDK will allow you to watermark locally, via a licensed downloadable package, so that you do not need to send content to our servers if you do not wish to do so. Currently the public release only supports C2PA signing.
In order to prevent abuse of the watermark and of the ID space, we need to at a minimum license all use of the watermark. This ensures that your use of the watermark carries trust and value, and is not diluted by open-source users.
On average, expect that PawPrint will be 2-3 times cheaper than existing industry alternatives. We are able to provide these prices because our technology can scale far better.
The architecture of the neural network is simply better. We were able to design the watermark this way because (1) we have a solid fundamental understanding of what makes traditional watermarks work (our traditional image watermark was a winner in the 2026 JPEG Trust competition despite being up against a field of neural networks) and (2) we have a solid fundamental understanding of how machine learning works.
Soon, stay tuned!