Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model

Qi Song; Ziyuan Luo; Ka Chun  Cheung; Simon See; Renjie Wan

doi:10.1007/978-3-031-73247-8_4

Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model

Qi Song
, Ziyuan Luo
, Ka Chun Cheung
, Simon See
, Renjie Wan^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

5 Citations (Scopus)

Abstract

Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose NeRFProtector, which adopts a plug-and-play strategy to protect NeRF’s copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods.

Original language	English
Title of host publication	Computer Vision – ECCV 2024
Subtitle of host publication	18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XI
Editors	Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol
Place of Publication	Cham
Publisher	Springer
Pages	57-73
Number of pages	17
Edition	1st
ISBN (Electronic)	9783031732478
ISBN (Print)	9783031732461
DOIs	https://doi.org/10.1007/978-3-031-73247-8_4
Publication status	Published - 31 Oct 2024
Event	18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 29 Sept 2024 → 4 Oct 2024 https://eccv.ecva.net/Conferences/2024 (Conference Website) https://link.springer.com/book/10.1007/978-3-031-73232-4 (Conference Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Volume	15069
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th European Conference on Computer Vision, ECCV 2024
Country/Territory	Italy
City	Milan
Period	29/09/24 → 4/10/24
Internet address	https://eccv.ecva.net/Conferences/2024 (Conference Website) https://link.springer.com/book/10.1007/978-3-031-73232-4 (Conference Proceedings)

User-Defined Keywords

Copyright protection
Plug-and-play
NeRF

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-73247-8_4

Cite this

Song, Q., Luo, Z., Cheung, K. C., See, S., & Wan, R. (2024). Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XI (1st ed., pp. 57-73). (Lecture Notes in Computer Science; Vol. 15069). Springer. https://doi.org/10.1007/978-3-031-73247-8_4

Song, Qi ; Luo, Ziyuan ; Cheung, Ka Chun et al. / Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model. Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XI. editor / Aleš Leonardis ; Elisa Ricci ; Stefan Roth ; Olga Russakovsky ; Torsten Sattler ; Gül Varol. 1st. ed. Cham : Springer, 2024. pp. 57-73 (Lecture Notes in Computer Science).

@inproceedings{1364a68b39184bd49bd6ea98308db367,

title = "Protecting NeRFs{\textquoteright} Copyright via Plug-And-Play Watermarking Base Model",

abstract = "Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose NeRFProtector, which adopts a plug-and-play strategy to protect NeRF{\textquoteright}s copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods.",

keywords = "Copyright protection, Plug-and-play, NeRF",

author = "Qi Song and Ziyuan Luo and Cheung, \{Ka Chun\} and Simon See and Renjie Wan",

note = "This work was done at Renjie{\textquoteright}s Research Group at the Department of Computer Science of Hong Kong Baptist University. Renjie{\textquoteright}s Research Group is supported by the National Natural Science Foundation of China under Grant No. 62302415, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2022A1515110692, 2024A1515012822, and the Blue Sky Research Fund of HKBU under Grant No. BSRF/21-22/16. Publisher Copyright: {\textcopyright} 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG.; 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",

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editor = "Ale{\v s} Leonardis and Ricci, \{Elisa \} and Stefan Roth and Russakovsky, \{Olga \} and Torsten Sattler and Varol, \{G{\"u}l \}",

booktitle = "Computer Vision – ECCV 2024",

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Song, Q, Luo, Z, Cheung, KC, See, S & Wan, R 2024, Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model. in A Leonardis, E Ricci, S Roth, O Russakovsky, T Sattler & G Varol (eds), Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XI. 1st edn, Lecture Notes in Computer Science, vol. 15069, Springer, Cham, pp. 57-73, 18th European Conference on Computer Vision, ECCV 2024, Milan, Italy, 29/09/24. https://doi.org/10.1007/978-3-031-73247-8_4

Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model. / Song, Qi; Luo, Ziyuan; Cheung, Ka Chun et al.
Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XI. ed. / Aleš Leonardis; Elisa Ricci; Stefan Roth; Olga Russakovsky; Torsten Sattler; Gül Varol. 1st. ed. Cham: Springer, 2024. p. 57-73 (Lecture Notes in Computer Science; Vol. 15069).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model

AU - Song, Qi

AU - Luo, Ziyuan

AU - Cheung, Ka Chun

AU - See, Simon

AU - Wan, Renjie

N1 - This work was done at Renjie’s Research Group at the Department of Computer Science of Hong Kong Baptist University. Renjie’s Research Group is supported by the National Natural Science Foundation of China under Grant No. 62302415, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2022A1515110692, 2024A1515012822, and the Blue Sky Research Fund of HKBU under Grant No. BSRF/21-22/16. Publisher Copyright: © 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2024/10/31

Y1 - 2024/10/31

N2 - Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose NeRFProtector, which adopts a plug-and-play strategy to protect NeRF’s copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods.

AB - Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose NeRFProtector, which adopts a plug-and-play strategy to protect NeRF’s copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods.

KW - Copyright protection

KW - Plug-and-play

KW - NeRF

UR - https://www.scopus.com/pages/publications/85210022778

U2 - 10.1007/978-3-031-73247-8_4

DO - 10.1007/978-3-031-73247-8_4

M3 - Conference proceeding

SN - 9783031732461

T3 - Lecture Notes in Computer Science

SP - 57

EP - 73

BT - Computer Vision – ECCV 2024

A2 - Leonardis, Aleš

A2 - Ricci, Elisa

A2 - Roth, Stefan

A2 - Russakovsky, Olga

A2 - Sattler, Torsten

A2 - Varol, Gül

PB - Springer

CY - Cham

T2 - 18th European Conference on Computer Vision, ECCV 2024

Y2 - 29 September 2024 through 4 October 2024

ER -

Song Q, Luo Z, Cheung KC, See S, Wan R. Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model. In Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G, editors, Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XI. 1st ed. Cham: Springer. 2024. p. 57-73. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-73247-8_4

Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model

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User-Defined Keywords

UN SDGs

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