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Cited by 251Advanced Steganography and Watermarking TechniquesDigital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

The Stable Signature: Rooting Watermarks in Latent Diffusion Models

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Fernandez, Pierre, Couairon, Guillaume, Jégou, Hervé, Douze, Matthijs, Furon, Teddy·2023·2023 IEEE/CVF International Conference on Computer Vision (ICCV)
DOI 10.1109/iccv51070.2023.02053
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Abstract

Generative image modeling enables a wide range of applications but raises ethical concerns about responsible deployment. We introduce an active content tracing method combining image watermarking and Latent Diffusion Models. The goal is for all generated images to conceal an invisible watermark allowing for future detection and/or identification. The method quickly fine-tunes the latent decoder of the image generator, conditioned on a binary signature. A pre-trained watermark extractor recovers the hidden signature from any generated image and a statistical test then determines whether it comes from the generative model. We evaluate the invisibility and robustness of the watermarks on a variety of generation tasks, showing that the Stable Signature is robust to image modifications. For instance, it detects the origin of an image generated from a text prompt, then cropped to keep 10% of the content, with 90+% accuracy at a false positive rate below 10−6.

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  • Early diffusion-specific defenses emphasized robust watermarking within the latent diffusion process, embedding uniform and traceable signals directly into latent spaces or diffusion steps.

    “Early diffusionspecific defenses emphasized robust watermarking within the latent diffusion process, embedding uniform and traceable signals directly into latent spaces or diffusion steps [15], [213], [214], [215].”

    From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [213]

    The Introduction and Conclusion & Discussion support the broad claim that this work embeds robust, traceable watermarks in images generated by latent diffusion models. They do not support the stated mechanism: the paper says it fine-tunes the latent decoder and does not modify the diffusion process, rather than embedding signals directly into latent spaces or diffusion steps. Page numbers are not available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Algorithms for identifying and watermarking AI-generated content may not encompass the extent and nuance of creators' attribution needs.

    “A creator-centric how of AI attribution remains an open question – while algorithms for identifying and watermarking AI-generated content offer useful technological approaches [20, 28, 36], they may not encompass the extent and nuance of creators’ attribution needs.”

    From Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation (2025) · cites this paper as [28]

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Cited by

Sorted by citations

Authors
Fernandez, P., Couairon, G., Jégou, H., Douze, M., & Furon, T.
Affiliations
  • Meta (United States) (US)
  • Sorbonne Université (FR)
  • Meta AI
  • Centre Inria de l'Université de Rennes (FR)
ORCID
Venue
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE
Published
2023-10-01
Pages
22409-22420
DOI
10.1109/iccv51070.2023.02053
OpenAlex
W4390872921
Cited by
251
References
20
Citations by year
2026: 101 · 2025: 90 · 2024: 52 · 2023: 8
FWCI
25.56
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • HAL (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
Agence Nationale de la Recherche (ANR-20-CHIA-0011)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords
image watermarking, latent diffusion models, watermark extraction, watermark robustness, image provenance, generative image models, watermark invisibility

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