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Generation of Gradient-Preserving Images allowing HOG Feature Extraction
In this paper, we propose a method for generating visually protected images, referred to as gradient-preserving images.
Difficulty in estimating visual information from randomly sampled images
In this paper, we evaluate dimensionality reduction methods in terms of difficulty in estimating visual information on original images from dimensionally reduced ones.
HOG feature extraction from encrypted images for privacy-preserving machine learning
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.
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