SynthID-Image: Image watermarking at internet scale
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915544499748864 |
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| author | Gowal, Sven Bunel, Rudy Stimberg, Florian Stutz, David Ortiz-Jimenez, Guillermo Kouridi, Christina Vecerik, Mel Hayes, Jamie Rebuffi, Sylvestre-Alvise Bernard, Paul Gamble, Chris Horváth, Miklós Z. Kaczmarczyck, Fabian Kaskasoli, Alex Petrov, Aleksandar Shumailov, Ilia Thotakuri, Meghana Wiles, Olivia Yung, Jessica Ahmed, Zahra Martin, Victor Rosen, Simon Savčak, Christopher Senoner, Armin Vyas, Nidhi Kohli, Pushmeet |
| author_facet | Gowal, Sven Bunel, Rudy Stimberg, Florian Stutz, David Ortiz-Jimenez, Guillermo Kouridi, Christina Vecerik, Mel Hayes, Jamie Rebuffi, Sylvestre-Alvise Bernard, Paul Gamble, Chris Horváth, Miklós Z. Kaczmarczyck, Fabian Kaskasoli, Alex Petrov, Aleksandar Shumailov, Ilia Thotakuri, Meghana Wiles, Olivia Yung, Jessica Ahmed, Zahra Martin, Victor Rosen, Simon Savčak, Christopher Senoner, Armin Vyas, Nidhi Kohli, Pushmeet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynthID-Image: Image watermarking at internet scale Gowal, Sven Bunel, Rudy Stimberg, Florian Stutz, David Ortiz-Jimenez, Guillermo Kouridi, Christina Vecerik, Mel Hayes, Jamie Rebuffi, Sylvestre-Alvise Bernard, Paul Gamble, Chris Horváth, Miklós Z. Kaczmarczyck, Fabian Kaskasoli, Alex Petrov, Aleksandar Shumailov, Ilia Thotakuri, Meghana Wiles, Olivia Yung, Jessica Ahmed, Zahra Martin, Victor Rosen, Simon Savčak, Christopher Senoner, Armin Vyas, Nidhi Kohli, Pushmeet Cryptography and Security Artificial Intelligence 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. |
| title | SynthID-Image: Image watermarking at internet scale |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2510.09263 |