Oulu Remote-Photoplethysmography Physical Domain Attacks Database (ORPDAD)
Savic, Marko; Zhao, Guoying (2024-12-04)
Avaa tiedosto
Sisältö avataan julkiseksi: 04.12.2025
Springer
04.12.2024
Savic, M., Zhao, G. (2025). Oulu Remote-Photoplethysmography Physical Domain Attacks Database (ORPDAD). In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15131. Springer, Cham. https://doi.org/10.1007/978-3-031-73464-9_4
https://rightsstatements.org/vocab/InC/1.0/
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXXIII. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-73464-9_4
https://rightsstatements.org/vocab/InC/1.0/
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXXIII. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-73464-9_4
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412117176
https://urn.fi/URN:NBN:fi:oulu-202412117176
Tiivistelmä
Abstract
Remote photoplethysmography (rPPG) is an emerging technology that can detect the pulse rate remotely from face videos. However, it is easily influenced by the recording environment, as robustness to noise is still an open problem. This vulnerability can therefore be exploited to inject fake signals or impair predictions physically. In this study we propose the first dataset containing a wide set of physical domain attack scenarios divided in three categories (illumination, movement, concealment) that directly target the main weaknesses of rPPG. We propose the Oulu rPPG Physical Domain Attacks Database (ORPDAD) as a benchmark for evaluation of robustness to physical attacks. We perform extensive experiments on conventional hand-crafted and deep learning (end-to-end, non-end-to-end, CNN, transformer, self-supervised) methods and study their susceptibility to the attacks. We conclude by discussing the most critical vulnerabilities discovered and stress the importance of designing more secure solutions. Our code and instructions for requesting the dataset (with trained models) are available at: https://github.com/marukosan93/ORPDAD/.
Remote photoplethysmography (rPPG) is an emerging technology that can detect the pulse rate remotely from face videos. However, it is easily influenced by the recording environment, as robustness to noise is still an open problem. This vulnerability can therefore be exploited to inject fake signals or impair predictions physically. In this study we propose the first dataset containing a wide set of physical domain attack scenarios divided in three categories (illumination, movement, concealment) that directly target the main weaknesses of rPPG. We propose the Oulu rPPG Physical Domain Attacks Database (ORPDAD) as a benchmark for evaluation of robustness to physical attacks. We perform extensive experiments on conventional hand-crafted and deep learning (end-to-end, non-end-to-end, CNN, transformer, self-supervised) methods and study their susceptibility to the attacks. We conclude by discussing the most critical vulnerabilities discovered and stress the importance of designing more secure solutions. Our code and instructions for requesting the dataset (with trained models) are available at: https://github.com/marukosan93/ORPDAD/.
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