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Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions., Comment: To appear in IEEE Transactions on Visualization and Computer Graphics

Advances in face swapping have enabled the automatic generation of highly realistic faces. Yet face swaps are perceived differently than when looking at real faces, with key differences in viewer behavior surrounding the eyes. Face swapping algorithms generally place no emphasis on the eyes, relying on pixel or feature matching losses that consider the entire face to guide the training process. We further investigate viewer perception of face swaps, focusing our analysis on the presence of an uncanny valley effect. We additionally propose a novel loss equation for the training of face swapping models, leveraging a pretrained gaze estimation network to directly improve representation of the eyes. We confirm that viewed face swaps do elicit uncanny responses from viewers. Our proposed improvements significant reduce viewing angle errors between face swaps and their source material. Our method additionally reduces the prevalence of the eyes as a deciding factor when viewers perform deepfake detection tasks. Our findings have implications on face swapping for special effects, as digital avatars, as privacy mechanisms, and more; negative responses from users could limit effectiveness in said applications. Our gaze improvements are a first step towards alleviating negative viewer perceptions via a targeted approach., Comment: Accepted to Computers and Graphics Special Issue: Eye Gaze Visualization, Interaction, Synthesis, and Analysis

Face swapping combines one face's identity with another face's non-appearance attributes (expression, head pose, lighting) to generate a synthetic face. This technology is rapidly improving, but falls flat when reconstructing some attributes, particularly gaze. Image-based loss metrics that consider the full face do not effectively capture the perceptually important, yet spatially small, eye regions. Improving gaze in face swaps can improve naturalness and realism, benefiting applications in entertainment, human computer interaction, and more. Improved gaze will also directly improve Deepfake detection efforts, serving as ideal training data for classifiers that rely on gaze for classification. We propose a novel loss function that leverages gaze prediction to inform the face swap model during training and compare against existing methods. We find all methods to significantly benefit gaze in resulting face swaps., Comment: Published in 2023 Symposium on Eye Tracking Research and Applications (ETRA '23), May 30-June 2, 2023, Tubingen, Germany, https://doi.org/10.1145/3588015.3588416

Latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention and cognitive processes in VR. Despite these possibilities, eye-tracking data also reveal privacy-sensitive attributes of users when it is combined with the information about the presented stimulus. To address these possibilities and potential privacy issues, in this survey, we first cover major works in eye tracking, VR, and privacy areas between the years 2012 and 2022. While eye tracking in the VR part covers the complete pipeline of eye-tracking methodology from pupil detection and gaze estimation to offline use and analyses, as for privacy and security, we focus on eye-based authentication as well as computational methods to preserve the privacy of individuals and their eye-tracking data in VR. Later, taking all into consideration, we draw three main directions for the research community by mainly focusing on privacy challenges. In summary, this survey provides an extensive literature review of the utmost possibilities with eye tracking in VR and the privacy implications of those possibilities., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Alertness monitoring in the context of driving improves safety and saves lives. Computer vision based alertness monitoring is an active area of research. However, the algorithms and datasets that exist for alertness monitoring are primarily aimed at younger adults (18-50 years old). We present a system for in-vehicle alertness monitoring for older adults. Through a design study, we ascertained the variables and parameters that are suitable for older adults traveling independently in Level 5 vehicles. We implemented a prototype traveler monitoring system and evaluated the alertness detection algorithm on ten older adults (70 years and older). We report on the system design and implementation at a level of detail that is suitable for the beginning researcher or practitioner. Our study suggests that dataset development is the foremost challenge for developing alertness monitoring systems targeted at older adults. This study is the first of its kind for a hitherto under-studied population and has implications for future work on algorithm development and system design through participatory methods., Comment: 12 pages, 14 figures, 6 tables

With rapid advancements in image generation technology, face swapping for privacy protection has emerged as an active area of research. The ultimate benefit is improved access to video datasets, e.g. in healthcare settings. Recent literature has proposed deep network-based architectures to perform facial swaps and reported the associated reduction in facial recognition accuracy. However, there is not much reporting on how well these methods preserve the types of semantic information needed for the privatized videos to remain useful for their intended application. Our main contribution is a novel end-to-end face swapping pipeline for recorded videos of standardized assessments of autism symptoms in children. Through this design, we are the first to provide a methodology for assessing the privacy-utility trade-offs for the face swapping approach to patient privacy protection. Our methodology can show, for example, that current deep network based face swapping is bottle-necked by face detection in real world videos, and the extent to which gaze and expression information is preserved by face swaps relative to baseline privatization methods such as blurring., Comment: 15 pages, 9 figures

Eye-tracking technology is being increasingly integrated into mixed reality devices. Although critical applications are being enabled, there are significant possibilities for violating user privacy expectations. We show that there is an appreciable risk of unique user identification even under natural viewing conditions in virtual reality. This identification would allow an app to connect a user's personal ID with their work ID without needing their consent, for example. To mitigate such risks we propose a framework that incorporates gatekeeping via the design of the application programming interface and via software-implemented privacy mechanisms. Our results indicate that these mechanisms can reduce the rate of identification from as much as 85% to as low as 30%. The impact of introducing these mechanisms is less than 1.5$^\circ$ error in gaze position for gaze prediction. Gaze data streams can thus be made private while still allowing for gaze prediction, for example, during foveated rendering. Our approach is the first to support privacy-by-design in the flow of eye-tracking data within mixed reality use cases., Comment: 12 pages, 4 figures, to appear in IEEE TVCG Special Issue on IEEE VR 2021

The gaze behavior of virtual avatars is critical to social presence and perceived eye contact during social interactions in Virtual Reality. Virtual Reality headsets are being designed with integrated eye tracking to enable compelling virtual social interactions. This paper shows that the near infra-red cameras used in eye tracking capture eye images that contain iris patterns of the user. Because iris patterns are a gold standard biometric, the current technology places the user's biometric identity at risk. Our first contribution is an optical defocus based hardware solution to remove the iris biometric from the stream of eye tracking images. We characterize the performance of this solution with different internal parameters. Our second contribution is a psychophysical experiment with a same-different task that investigates the sensitivity of users to a virtual avatar's eye movements when this solution is applied. By deriving detection threshold values, our findings provide a range of defocus parameters where the change in eye movements would go unnoticed in a conversational setting. Our third contribution is a perceptual study to determine the impact of defocus parameters on the perceived eye contact, attentiveness, naturalness, and truthfulness of the avatar. Thus, if a user wishes to protect their iris biometric, our approach provides a solution that balances biometric protection while preventing their conversation partner from perceiving a difference in the user's virtual avatar. This work is the first to develop secure eye tracking configurations for VR/AR/XR applications and motivates future work in the area., Comment: 11 pages, 10 figures, to appear in IEEE TVCG Special Issue on IEEE VR 2020

As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals' data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of (noise-free) heatmaps and show that they do not guarantee privacy. We further propose two noise mechanisms that guarantee privacy and analyze their privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is an elegant solution to preserve privacy for heatmaps. Our results have implications for interdisciplinary research to create differentially private mechanisms for eye tracking., Comment: 10 pages including appendix