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Privacy in Eye Tracking Research with Stable Diffusion

Author:

Kuno KurzhalsAuthors Info & Claims

ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications

Article No.: 70, Pages 1 - 7

https://doi.org/10.1145/3588015.3589842

Published: 30 May 2023 Publication History

Abstract

Image-generative models take textual prompts as input and generate almost arbitrary image content based on the underlying training data. This technology is rapidly developing and produces better results with each new generation of trained models. Apart from the application to create artwork, we see potential in deploying such models for eye-tracking research with respect to anonymizing content in visual stimuli. One feature of such models is the ability to take an image as input and adjust content according to a prompt. Hence, privacy-preserving visualization of stimuli can be achieved for static images and videos by slightly adjusting content to anonymize persons, text, and other sensible sources. In this work, we will discuss how this process can be applied to the presentation and dissemination of results with respect to privacy issues resulting from eye-tracking experiments.

References

[1]

Pascal Birnstill, Daoyuan Ren, and Jürgen Beyerer. 2015. A user study on anonymization techniques for smart video surveillance. In Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). 1–6.

[2]

Tanja Blascheck, Kuno Kurzhals, Michael Raschke, Michael Burch, Daniel Weiskopf, and Thomas Ertl. 2017. Visualization of eye tracking data: A taxonomy and survey. In Computer Graphics Forum, Vol. 36. 260–284.

[3]

Bobby Chesney and Danielle Citron. 2019. Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review 107 (2019), 1753–1820.

[4]

Brendan David-John, Diane Hosfelt, Kevin Butler, and Eakta Jain. 2021. A privacy-preserving approach to streaming eye-tracking data. IEEE Transactions on Visualization and Computer Graphics 27, 5 (2021), 2555–2565.

[5]

Fabian Göbel, Kuno Kurzhals, Martin Raubal, and Victor R Schinazi. 2020. Gaze-aware mixed-reality: Addressing privacy issues with eye tracking. In CHI 2020 Workshop on Exploring Potentially Abusive Ethical, Social and Political Implications of Mixed Reality in HCI (CHI 2020).

[6]

Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840–6851.

[7]

Håkon Hukkelås, Rudolf Mester, and Frank Lindseth. 2019. DeepPrivacy: A generative adversarial network for face anonymization. In Advances in Visual Computing: International Symposium on Visual Computing. 565–578.

Digital Library

[8]

Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Yizhou Yu, and Mingli Song. 2019. Neural style transfer: A review. IEEE Transactions on Visualization and Computer Graphics 26, 11 (2019), 3365–3385.

[9]

Christina Katsini, Yasmeen Abdrabou, George E Raptis, Mohamed Khamis, and Florian Alt. 2020. The role of eye gaze in security and privacy applications: Survey and future HCI research directions. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–21.

Digital Library

[10]

Mohamed Khamis, Habiba Farzand, Marija Mumm, and Karola Marky. 2022. DeepFakes for Privacy: Investigating the Effectiveness of State-of-the-Art Privacy-Enhancing Face Obfuscation Methods. In Proceedings of the 2022 International Conference on Advanced Visual Interfaces. 1–5.

Digital Library

[11]

Kuno Kurzhals, Marcel Hlawatsch, Christof Seeger, and Daniel Weiskopf. 2016. Visual analytics for mobile eye tracking. IEEE Transactions on Visualization and Computer Graphics 23, 1 (2016), 301–310.

Digital Library

[12]

Daniel J Liebling and Sören Preibusch. 2014. Privacy considerations for a pervasive eye tracking world. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. 1169–1177.

Digital Library

[13]

Maxim Maximov, Ismail Elezi, and Laura Leal-Taixé. 2020. CIAGAN: Conditional identity anonymization generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5447–5456.

[14]

Aude Oliva and Antonio Torralba. 2006. Building the gist of a scene: The role of global image features in recognition. Progress in Brain Research 155 (2006), 23–36.

[15]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-Resolution Image Synthesis With Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10684–10695.

[16]

Julian Steil, Inken Hagestedt, Michael Xuelin Huang, and Andreas Bulling. 2019a. Privacy-aware eye tracking using differential privacy. In Proceedings of the ACM Symposium on Eye Tracking Research and Applications. 1–9.

Digital Library

[17]

Julian Steil, Marion Koelle, Wilko Heuten, Susanne Boll, and Andreas Bulling. 2019b. Privaceye: privacy-preserving head-mounted eye tracking using egocentric scene image and eye movement features. In Proceedings of the ACM Symposium on Eye Tracking Research and Applications. 1–10.

Digital Library

[18]

Daksh Thapar, Aditya Nigam, and Chetan Arora. 2021. Anonymizing egocentric videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2320–2329.

[19]

Lvmin Zhang and Maneesh Agrawala. 2023. Adding Conditional Control to Text-to-Image Diffusion Models. arXiv preprint arXiv:2302.05543 (2023), 1–33.

Cited By

Malm SRönnbäck VHåkansson ALe MWojtulewicz KCarlsson NAyday EVaidya J(2024)RAD: Realistic Anonymization of Images Using Stable DiffusionProceedings of the 23rd Workshop on Privacy in the Electronic Society10.1145/3689943.3695048(193-211)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3689943.3695048
Kurzhals K(2024)Anonymizing eye-tracking stimuli with stable diffusionComputers & Graphics10.1016/j.cag.2024.103898119(103898)Online publication date: Apr-2024
https://doi.org/10.1016/j.cag.2024.103898

Index Terms

Privacy in Eye Tracking Research with Stable Diffusion
1. Human-centered computing
  1. Visualization
    1. Visualization techniques

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Information & Contributors

Information

Published In

cover image ACM Conferences

ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications

May 2023

441 pages

ISBN:9798400701504

DOI:10.1145/3588015

Editors:
Enkelejda Kasneci
Technical University Munich, Germany
,
Frederick Shic
University of Washington, USA
,
Mohamed Khamis
University of Glasgow, Scotland

Copyright © 2023 ACM.

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Publication History

Published: 30 May 2023

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Deutsche Forschungsgemeinschaft

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ETRA '23

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ETRA '23: 2023 Symposium on Eye Tracking Research and Applications

May 30 - June 2, 2023

Tubingen, Germany

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Cited By

Malm SRönnbäck VHåkansson ALe MWojtulewicz KCarlsson NAyday EVaidya J(2024)RAD: Realistic Anonymization of Images Using Stable DiffusionProceedings of the 23rd Workshop on Privacy in the Electronic Society10.1145/3689943.3695048(193-211)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3689943.3695048
Kurzhals K(2024)Anonymizing eye-tracking stimuli with stable diffusionComputers & Graphics10.1016/j.cag.2024.103898119(103898)Online publication date: Apr-2024
https://doi.org/10.1016/j.cag.2024.103898

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