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Generation of Gradient-Preserving Images allowing HOG Feature Extraction

arXiv 2104.01350 ↗First posted 3 Apr 2021cs.CVcited by 0

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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.

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.

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References · 3

  1. 2019Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted Domaincited by 19
  2. 2020A Privacy-Preserving Machine Learning Scheme Using EtC Imagescited by 9
  3. 2019Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformationcited by 5

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