• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Liu Yanxiao, Wu Ping, Sun Qindong. Secret Image Sharing Schemes Based on Region Convolution Neural Network[J]. Journal of Computer Research and Development, 2021, 58(5): 1065-1074. DOI: 10.7544/issn1000-1239.2021.20200898
Citation: Liu Yanxiao, Wu Ping, Sun Qindong. Secret Image Sharing Schemes Based on Region Convolution Neural Network[J]. Journal of Computer Research and Development, 2021, 58(5): 1065-1074. DOI: 10.7544/issn1000-1239.2021.20200898

Secret Image Sharing Schemes Based on Region Convolution Neural Network

Funds: 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).
More Information
  • Published Date: April 30, 2021
  • 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.
  • Related Articles

    [1]Yue Wenjing, Qu Wenwen, Lin Kuan, Wang Xiaoling. Survey of Cardinality Estimation Techniques Based on Machine Learning[J]. Journal of Computer Research and Development, 2024, 61(2): 413-427. DOI: 10.7544/issn1000-1239.202220649
    [2]Li Jianing, Xiong Ruibin, Lan Yanyan, Pang Liang, Guo Jiafeng, Cheng Xueqi. Overview of the Frontier Progress of Causal Machine Learning[J]. Journal of Computer Research and Development, 2023, 60(1): 59-84. DOI: 10.7544/issn1000-1239.202110780
    [3]Wang Ye, Chen Junwu, Xia Xin, Jiang Bo. Intelligent Requirements Elicitation and Modeling: A Literature Review[J]. Journal of Computer Research and Development, 2021, 58(4): 683-705. DOI: 10.7544/issn1000-1239.2021.20200740
    [4]Chen Jinyin, Chen Yipeng, Chen Yiming, Zheng Haibin, Ji Shouling, Shi Jie, Cheng Yao. Fairness Research on Deep Learning[J]. Journal of Computer Research and Development, 2021, 58(2): 264-280. DOI: 10.7544/issn1000-1239.2021.20200758
    [5]Cheng Keyang, Wang Ning, Shi Wenxi, Zhan Yongzhao. Research Advances in the Interpretability of Deep Learning[J]. Journal of Computer Research and Development, 2020, 57(6): 1208-1217. DOI: 10.7544/issn1000-1239.2020.20190485
    [6]Liu Chenyi, Xu Mingwei, Geng Nan, Zhang Xiang. A Survey on Machine Learning Based Routing Algorithms[J]. Journal of Computer Research and Development, 2020, 57(4): 671-687. DOI: 10.7544/issn1000-1239.2020.20190866
    [7]Liu Junxu, Meng Xiaofeng. Survey on Privacy-Preserving Machine Learning[J]. Journal of Computer Research and Development, 2020, 57(2): 346-362. DOI: 10.7544/issn1000-1239.2020.20190455
    [8]Ji Shouling, Li Jinfeng, Du Tianyu, Li Bo. Survey on Techniques, Applications and Security of Machine Learning Interpretability[J]. Journal of Computer Research and Development, 2019, 56(10): 2071-2096. DOI: 10.7544/issn1000-1239.2019.20190540
    [9]Meng Xiaofeng, Ma Chaohong, Yang Chen. Survey on Machine Learning for Database Systems[J]. Journal of Computer Research and Development, 2019, 56(9): 1803-1820. DOI: 10.7544/issn1000-1239.2019.20190446
    [10]Yu Kai, Jia Lei, Chen Yuqiang, and Xu Wei. Deep Learning: Yesterday, Today, and Tomorrow[J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804.
  • Cited by

    Periodical cited type(5)

    1. 周军芽,吴进伟,吴广飞,张何为. 基于Bi-LSTM神经网络的短文本敏感词识别方法. 武汉理工大学学报(信息与管理工程版). 2024(02): 312-316 .
    2. 石新满,胡广林,邵鑫,赵新爽,张思慧,乔晓. 基于人工智能大语言模型技术的电网优化运行应用分析. 自动化与仪器仪表. 2024(08): 180-184 .
    3. 李卓卓,蒋雨萌. 信息隐私量表对象、指标和应用的研究与展望. 情报理论与实践. 2024(10): 41-52 .
    4. 谭九生,李猛. 人机融合智能的伦理风险及其适应性治理. 昆明理工大学学报(社会科学版). 2022(03): 37-45 .
    5. 潘旭东,张谧,杨珉. 基于神经元激活模式控制的深度学习训练数据泄露诱导. 计算机研究与发展. 2022(10): 2323-2337 . 本站查看

    Other cited types(7)

Catalog

    Article views (653) PDF downloads (320) Cited by(12)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return