Human-Imperceptible Identification with Learnable Lensless Imaging
- URL: http://arxiv.org/abs/2302.02255v1
- Date: Sat, 4 Feb 2023 22:58:46 GMT
- Title: Human-Imperceptible Identification with Learnable Lensless Imaging
- Authors: Thuong Nguyen Canh, Trung Thanh Ngo, Hajime Nagahara
- Abstract summary: We propose a learnable lensless imaging framework that protects visual privacy while maintaining recognition accuracy.
To make captured images imperceptible to humans, we designed several loss functions based on total variation, invertibility, and the restricted isometry property.
- Score: 12.571999330435801
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Lensless imaging protects visual privacy by capturing heavily blurred images
that are imperceptible for humans to recognize the subject but contain enough
information for machines to infer information. Unfortunately, protecting visual
privacy comes with a reduction in recognition accuracy and vice versa. We
propose a learnable lensless imaging framework that protects visual privacy
while maintaining recognition accuracy. To make captured images imperceptible
to humans, we designed several loss functions based on total variation,
invertibility, and the restricted isometry property. We studied the effect of
privacy protection with blurriness on the identification of personal identity
via a quantitative method based on a subjective evaluation. Moreover, we
validate our simulation by implementing a hardware realization of lensless
imaging with photo-lithographically printed masks.
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