# Scale Invariant Privacy Preserving Video via Wavelet Decomposition

arXiv: https://arxiv.org/abs/2211.03690 · HTML: https://mlsanity.com/paper/2211.03690/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Privacy-Preserving Human Activity Recognition from Extreme Low Resolution](https://mlsanity.com/paper/1604.03196/) (2016)
- [Ulixes: Facial Recognition Privacy with Adversarial Machine Learning](https://mlsanity.com/paper/2010.10242/) (2020)
- [The Smart2 Speaker Blocker: An Open-Source Privacy Filter for Connected Home Speakers](https://mlsanity.com/paper/1901.04879/) (2019)

## Abstract (arXiv)

Video surveillance has become ubiquitous in the modern world. Mobile devices, surveillance cameras, and IoT devices, all can record video that can violate our privacy. One proposed solution for this is privacy-preserving video, which removes identifying information from the video as it is produced. Several algorithms for this have been proposed, but all of them suffer from scale issues: in order to sufficiently anonymize near-camera objects, distant objects become unidentifiable. In this paper, we propose a scale-invariant method, based on wavelet decomposition.