# Generation of Gradient-Preserving Images allowing HOG Feature Extraction

arXiv: https://arxiv.org/abs/2104.01350 · HTML: https://mlsanity.com/paper/2104.01350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 3 Apr 2021.

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

- [Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted Domain](https://mlsanity.com/paper/1905.01827/) (2019)
- [A Privacy-Preserving Machine Learning Scheme Using EtC Images](https://mlsanity.com/paper/2007.08775/) (2020)
- [Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformation](https://mlsanity.com/paper/1908.07915/) (2019)

## Abstract (arXiv)

In this paper, we propose a method for generating visually protected images, referred to as gradient-preserving images. The protected images allow us to directly extract Histogram-of-Oriented-Gradients (HOG) features for privacy-preserving machine learning. In an experiment, HOG features extracted from gradient-preserving images are applied to a face recognition algorithm to demonstrate the effectiveness of the proposed method.