[Submitted on 31 Aug 2026]
Title:Lensless Gaze Is Not Private by Default: Auditing Identity Leakage Across Disclosure Surfaces
View a PDF of the paper titled Lensless Gaze Is Not Private by Default: Auditing Identity Leakage Across Disclosure Surfaces, by Rahul Vimalkanth and 1 other authors
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Abstract:Lensless near-eye sensing is often described as privacy-friendly because its coded measurements are visually unintelligible. Yet visual unintelligibility reflects human interpretation, not what a learned adversary can recover. We therefore treat identity privacy as a systems property of disclosure surfaces: representations crossing sensing, storage, computation, and output boundaries. We audit a simulated lensless gaze pipeline under a 36-subject known-gallery closed-set identification protocol with a fixed, known PSF; privacy from an unknown or varying optical key is outside our scope. Reported accuracies are empirical attack success rates under matched linear and MLP probes and do not upper-bound stronger adversaries. Simulated lensless measurements yield 96.7% top-1 identification versus 97.7% for matched original eye crops, while an MAE embedding retains 94.3%. Compression alone offers little protection: 8-D PCA and a matched 8-D bottleneck retain 93.2% and 91.8%, whereas separately trained 8-D GSPL bottlenecks yield 77.5% mean recovery across three seeds. A released 128-way gaze token lowers single-frame recovery to 38.1%, while its residual and continuous gaze output expose 62.1% and 72.6%, respectively. Under a source-frame-disjoint tiled protocol, token summaries reach 39.9% at T=25, showing that repeated-output risk depends on representation and aggregation. These rates reflect all subject-correlated information in the evaluated dataset, including acquisition and behavioral cues, rather than isolating intrinsic ocular biometrics. Ordinary least squares residualization against a six-dimensional crop geometry and intensity summary still leaves lensless recovery at 95.1%. Our results show that privacy claims for lensless sensing must be tested at disclosure boundaries rather than inferred from appearance.
Comments: 16 pages, 5 figures. Accepted at the PFATCV Workshop, ECCV 2026. Code available at this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.09188 [cs.CV]
(or arXiv:2609.09188v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.09188
arXiv-issued DOI via DataCite
Submission history
From: Rahul Vimalkanth [view email] [v1] Mon, 31 Aug 2026 19:31:01 UTC (22,804 KB)
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