ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (5): 1065-1074.doi: 10.7544/issn1000-1239.2021.20200898

所属专题: 2021人工智能安全与隐私保护技术专题

• 信息安全 • 上一篇    下一篇

基于区域卷积神经网络的图像秘密共享方案

刘雁孝1,2,吴萍,孙钦东1   

  1. 1(西安理工大学计算机科学与工程学院 西安 710048);2(广西可信软件重点实验室(桂林电子科技大学) 广西桂林 541004) (liuyanxiao@xaut.edu.cn)
  • 出版日期: 2021-05-01
  • 基金资助: 
    陕西省自然科学基础研究计划项目(2019JQ-736);陕西高校青年创新团队项目;广西可信软件重点实验室研究课题(KX202036);西安市科技计划项目(GXYD14.12,GXYD14.13)

Secret Image Sharing Schemes Based on Region Convolution Neural Network

Liu Yanxiao1,2, Wu Ping1, Sun Qindong1   

  1. 1(Faculty of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048);2(Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), Guilin, Guangxi 541004)
  • Online: 2021-05-01
  • Supported by: 
    This work was supported by the Natural Science Basic Research Project of Shaanxi Province (2019JQ-736), the Youth Innovation Team of Shaanxi Universities, the Guangxi Key Laboratory of Trusted Software (KX202036), and the Project of Xi’an Science and Technology Bureau (GXYD14.12, GXYD14.13).

摘要: 数字图像在如今网络高速发展时代已成为重要的信息载体,而对图像信息的安全保护也成为安全领域的重要研究课题.图像秘密共享方案是一种基于门限的密码学方案,能够为多个用户提供一种保护图像秘密信息的方案.该方案将秘密图像加密成若干个影子图像,分配给不同的用户.当用户的个数达到门限值后,原始图像可以被重构,否则用户无法获得原始图像的任何信息.图像信息的分类和识别是图像秘密共享的前提和基础,卷积神经网络(convolutional neural network, CNN)在图像分类和识别中具有较高的准确性和较快的速度.将基于卷积神经网络的图像识别和分类与图像秘密共享结合起来,将深度学习工具应用于图像信息保护,可以提高基于传统人工图像识别的图像保护方案的效率.首先采用区域卷积神经网络(region CNN, RCNN)模型对图像进行识别,根据所包含的信息内容将图像分割成重要性级别不同的若干区域,然后在此基础上构造2种图像秘密共享方案,渐进式重构图像秘密共享方案以及具有重要影子图像的图像秘密共享方案.其中重要性级别较高的图像区域在图像重构中需要较高的门限,这一特性使得图像秘密共享方案能够适用于更多的应用场景.与传统的基于人工特征的图像识别方法相比,神经网络的引用能够提升图像分类和识别的效率,从而进一步提升了图像秘密共享的应用价值.

关键词: 安全, 图像秘密共享, 门限, 图像识别, 卷积神经网络

Abstract: Digital image has become an important information carrier in the era of rapid network development, the security protection of image information has also become an important research topic in the security field. Secret image sharing is a threshold based approach that can protect confidential information in an image among multiple users. This scheme encrypts the secret image into several shadow images according a threshold and distributes them to different users. When the number of users reaches the threshold, the original image can be reconstructed, otherwise the user cannot obtain any information about the original image. The classification and recognition of image information is the premise and basis for image secret sharing, CNN (convolutional neural network) has higher accuracy and faster speed in image classification and recognition. In this paper, we combine CNN based image recognition and classification with secret image sharing together to applying the tool of deep learning in the field of information protection. First, we adopt Faster RCNN (region convolutional neural network) model to segment a secret image into multiple regions, where each region has dierent level, then progressive secret image sharing and secret image sharing with essential shadows are constructed, where the region with higher importance level needs higher threshold in reconstruction, this feature makes the image secret sharing scheme suitable for more application scenarios Compared with traditional image recognition methods based on artificial features, the use of Faster RCNN can greatly improve the efficiency of image classification and recognition, thereby further enhancing the application value of image secret sharing.

Key words: security, secret image sharing, threshold, image recognition, convolutional neural network (CNN)

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