SynthID-Image: Image watermarking at internet scale
We introduce SynthID-Image, a deep learning-based system for invisibly
watermarking AI-generated imagery. This paper documents the technical
desiderata, threat models, and practical challenges of deploying such a system
at internet scale, addressing key requirements of effectiveness, fidelity,
robustness, and security. SynthID-Image has been used to watermark over ten
billion images and video frames across Google's services and its corresponding
verification service is available to trusted testers. For completeness, we
present an experimental evaluation of an external model variant, SynthID-O,
which is available through partnerships. We benchmark SynthID-O against other
post-hoc watermarking methods from the literature, demonstrating
state-of-the-art performance in both visual quality and robustness to common
image perturbations. While this work centers on visual media, the conclusions
on deployment, constraints, and threat modeling generalize to other modalities,
including audio. This paper provides a comprehensive documentation for the
large-scale deployment of deep learning-based media provenance systems.
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Sven Gowal
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Rudy Bunel
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Florian Stimberg
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David Stutz
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Guillermo Ortiz-Jimenez
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Christina Kouridi
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Mel Vecerik
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Jamie Hayes
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Sylvestre-Alvise Rebuffi
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Paul Bernard
68ef7314121c484bb15d2e2b
Chris Gamble
68ef7314121c484bb15d2e2c
Miklós Z. Horváth
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Fabian Kaczmarczyck
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Alex Kaskasoli
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Aleksandar Petrov
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Ilia Shumailov
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Meghana Thotakuri
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Olivia Wiles
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Jessica Yung
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Zahra Ahmed
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Victor Martin
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Simon Rosen
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Christopher Savčak
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Armin Senoner
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Nidhi Vyas
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Pushmeet Kohli
SynthID-Image: Image watermarking at internet scale
We introduce SynthID-Image, a deep learning-based system for invisibly
watermarking AI-generated imagery. This paper documents the technical
desiderata, threat models, and practical challenges of deploying such a system
at internet scale, addressing key requirements of effectiveness, fidelity,
robustness, and security. SynthID-Image has been used to watermark over ten
billion images and video frames across Google's services and its corresponding
verification service is available to trusted testers. For completeness, we
present an experimental evaluation of an external model variant, SynthID-O,
which is available through partnerships. We benchmark SynthID-O against other
post-hoc watermarking methods from the literature, demonstrating
state-of-the-art performance in both visual quality and robustness to common
image perturbations. While this work centers on visual media, the conclusions
on deployment, constraints, and threat modeling generalize to other modalities,
including audio. This paper provides a comprehensive documentation for the
large-scale deployment of deep learning-based media provenance systems.
SynthID-Image, a deep learning system for watermarking AI-generated imagery, demonstrates state-of-the-art performance in visual quality and robustness, and is deployed across Google's services.
deep learning watermarking AI-generated imagery threat models robustness security visual quality image perturbations media provenance systems