HOG feature extraction from encrypted images for privacy-preserving machine learning

TL;DR

This paper introduces a method to extract HOG features directly from encrypted images, enabling privacy-preserving machine learning without decrypting the images, demonstrated on face recognition tasks with SVM classifiers.

Contribution

A novel block-based HOG feature extraction technique from encrypted images that allows machine learning without compromising privacy.

Findings

Effective face recognition using encrypted images with SVM classifiers.

The method maintains accuracy comparable to traditional HOG extraction.

Supports privacy-preserving machine learning in cloud environments.

Abstract

In this paper, we propose an extraction method of HOG (histograms-of-oriented-gradients) features from encryption-then-compression (EtC) images for privacy-preserving machine learning, where EtC images are images encrypted by a block-based encryption method proposed for EtC systems with JPEG compression, and HOG is a feature descriptor used in computer vision for the purpose of object detection and image classification. Recently, cloud computing and machine learning have been spreading in many fields. However, the cloud computing has serious privacy issues for end users, due to unreliability of providers and some accidents. Accordingly, we propose a novel block-based extraction method of HOG features, and the proposed method enables us to carry out any machine learning algorithms without any influence, under some conditions. In an experiment, the proposed method is applied to a face image recognition problem under the use of two kinds of classifiers: linear support vector machine (SVM), gaussian SVM, to demonstrate the effectiveness.