A Subject-Sensitive Perceptual Hash Based on MUM-Net for the Integrity Authentication of High Resolution Remote Sensing Images release_qy4bp5pymbgx3drfbefgouvelm

by Kaimeng Ding, Yueming Liu, Qin Xu, Fuqiang Lu

Published in ISPRS International Journal of Geo-Information by MDPI AG.

2020   p485

Abstract

Data security technology is of great significance to the application of high resolution remote sensing image (HRRS) images. As an important data security technology, perceptual hash overcomes the shortcomings of cryptographic hashing that is not robust and can achieve integrity authentication of HRRS images based on perceptual content. However, the existing perceptual hash does not take into account whether the user focuses on certain types of information of the HRRS image. In this paper, we introduce the concept of subject-sensitive perceptual hash, which can be seen as a special case of conventional perceptual hash, for the integrity authentication of HRRS image. To achieve subject-sensitive perceptual hash, we propose a new deep convolutional neural network architecture, named MUM-Net, for extracting robust features of HRRS images. MUM-Net is the core of perceptual hash algorithm, and it uses focal loss as the loss function to overcome the imbalance between the positive and negative samples in the training samples. The robust features extracted by MUM-Net are further compressed and encoded to obtain the perceptual hash sequence of HRRS image. Experiments show that our algorithm has higher tamper sensitivity to subject-related malicious tampering, and the robustness is improved by about 10% compared to the existing U-net-based algorithm; compared to other deep learning-based algorithms, this algorithm achieves a better balance between robustness and tampering sensitivity, and has better overall performance.
In application/xml+jats format

Archived Files and Locations

application/pdf  7.1 MB
file_bwd4bzrg7fez7e3y6qc23p4ida
res.mdpi.com (publisher)
web.archive.org (webarchive)

Web Captures

https://www.mdpi.com/2220-9964/9/8/485/htm
2022-07-18 23:11:21 | 62 resources
webcapture_jfvpxy5eszbxrhhxtfzjtcwjwm
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2020-08-11
Language   en ?
Journal Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2220-9964
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 848572c6-ba37-44d8-98fa-2b6ca2ceea51
API URL: JSON