Abstract
Recent developments in hardware, computer graphics, and AI may soon enable AR/VR head-mounted displays (HMDs) to become everyday devices like smartphones and tablets. Eye trackers within HMDs provide a special opportunity for such setups as it is possible to facilitate gaze-based research and interaction. However, estimating users' gaze information often requires raw eye images and videos that contain iris textures, which are considered a gold standard biometric for user authentication, and this raises privacy concerns. Previous research in the eye-tracking community focused on obfuscating iris textures while keeping utility tasks such as gaze estimation accurate. Despite these attempts, there is no comprehensive benchmark that evaluates state-of-the-art approaches. Considering all, in this paper, we benchmark blurring, noising, downsampling, rubber sheet model, and iris style transfer to obfuscate user identity, and compare their impact on image quality, privacy, utility, and risk of imposter attack on two datasets. We use eye segmentation and gaze estimation as utility tasks, and reduction in iris recognition accuracy as a measure of privacy protection, and false acceptance rate to estimate risk of attack. Our experiments show that canonical image processing methods like blurring and noising cause a marginal impact on deep learning-based tasks. While downsampling, rubber sheet model, and iris style transfer are effective in hiding user identifiers, iris style transfer, with higher computation cost, outperforms others in both utility tasks, and is more resilient against spoof attacks. Our analyses indicate that there is no universal optimal approach to balance privacy, utility, and computation burden. Therefore, we recommend practitioners consider the strengths and weaknesses of each approach, and possible combinations of those to reach an optimal privacy-utility trade-off.
Abstract (translated)
近期在硬件、计算机图形和人工智能方面的进展可能会使增强现实/虚拟现实头戴式显示器（HMD）成为像智能手机和平板电脑一样日常使用的设备。HMD 中的眼动追踪器为这种设置提供了特殊的机会，因为它可以促进基于注视的研究和交互。然而，估计用户的目光信息通常需要包含虹膜纹理的原始眼图像和视频，而这些被视作用于身份认证的标准生物特征数据，这引发了隐私方面的担忧。此前，在眼动追踪领域的研究主要集中在模糊化虹膜纹理的同时保持诸如视线估计等实用任务的准确性。尽管有这些尝试，但尚无全面评估前沿方法的基准测试。 考虑到所有因素，本文对模糊、噪声处理、降采样、橡胶片模型和虹膜风格转换这五种技术进行基准测试，以混淆用户身份，并比较它们在两个数据集上对图像质量、隐私性、实用性和冒充攻击风险的影响。我们使用眼睛分割和注视估计作为实用性任务，将虹膜识别准确性的降低视为隐私保护的衡量标准，并用错误接受率来估算攻击的风险。 我们的实验表明，传统的图像处理方法如模糊和添加噪声对于基于深度学习的任务影响甚微。然而，降采样、橡胶片模型以及虹膜风格转换在隐藏用户标识符方面都表现出色，其中虹膜风格转换以更高的计算成本为代价，在实用性任务中表现优于其他技术，并且更能抵御冒充攻击。 我们的分析表明，没有一种普遍适用的方法可以在隐私性、实用性和计算负担之间达到平衡。因此，我们建议实践者应考虑每种方法的优点和缺点，以及这些方法可能的组合方式，以实现最佳的隐私与实用性权衡。
URL
https://arxiv.org/abs/2504.10267
