# Human-Imperceptible Identification with Learnable Lensless Imaging

arXiv: https://arxiv.org/abs/2302.02255 · HTML: https://mlsanity.com/paper/2302.02255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 4 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Adversarial Examples in Modern Machine Learning: A Review](https://mlsanity.com/paper/1911.05268/) (2019)
- [DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing](https://mlsanity.com/paper/1607.03343/) (2016)
- [Privacy-Preserving Action Recognition using Coded Aperture Videos](https://mlsanity.com/paper/1902.09085/) (2019)
- [Lensless Imaging by Compressive Sensing](https://mlsanity.com/paper/1305.7181/) (2013)
- [CS-VQA: Visual Question Answering with Compressively Sensed Images](https://mlsanity.com/paper/1806.03379/) (2018)
- [No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image](https://mlsanity.com/paper/1908.10087/) (2019)
- [Restricted Structural Random Matrix for Compressive Sensing](https://mlsanity.com/paper/2002.07346/) (2020)
- [Multi-Scale Deep Compressive Imaging](https://mlsanity.com/paper/2008.00802/) (2020)

## Abstract (arXiv)

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.