Home / Journals / JIHPP / Vol.2, No.4, 2020
  • Open Access

    ARTICLE

    Image Denoising with GAN Based Model

    Peizhu Gong, Jin Liu*, Shiqi Lv
    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 155-163, 2020, DOI:10.32604/jihpp.2020.010453
    Abstract Image denoising is often used as a preprocessing step in computer vision tasks, which can help improve the accuracy of image processing models. Due to the imperfection of imaging systems, transmission media and recording equipment, digital images are often contaminated with various noises during their formation, which troubles the visual effects and even hinders people’s normal recognition. The pollution of noise directly affects the processing of image edge detection, feature extraction, pattern recognition, etc., making it difficult for people to break through the bottleneck by modifying the model. Many traditional filtering methods have shown poor performance since they do not… More >

  • Open Access

    REVIEW

    A Survey on Machine Learning in Chemical Spectral Analysis

    Dongfang Yu, Jinwei Wang*
    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 165-174, 2020, DOI:10.32604/jihpp.2020.010466
    Abstract Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms, in particular convolutional networks. Hence, this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images, and overviews the current application, development and breakthrough in different spectral characterization. Brief categorization of reviewed literatures is provided for studies per application apparatus: X-Ray spectra, UV-Vis-IR spectra, Micro-scope, Raman spectra, Photoluminescence spectrum. End with the overview of existing circumstances in this research area, we provide unique insight and promising directions for the chemical imaging field to fully couple… More >

  • Open Access

    REVIEW

    A Survey on Recent Advances in Privacy Preserving Deep Learning

    Siran Yin1,2, Leiming Yan1,2,*, Yuanmin Shi1,2, Yaoyang Hou1,2, Yunhong Zhang1,2
    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 175-185, 2020, DOI:10.32604/jihpp.2020.010780
    Abstract Deep learning based on neural networks has made new progress in a wide variety of domain, however, it is lack of protection for sensitive information. The large amount of data used for training is easy to cause leakage of private information, thus the attacker can easily restore input through the representation of latent natural language. The privacy preserving deep learning aims to solve the above problems. In this paper, first, we introduce how to reduce training samples in order to reduce the amount of sensitive information, and then describe how to unbiasedly represent the data with respect to specific attributes,… More >

  • Open Access

    ARTICLE

    A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

    Xingxing Cao1, Liming Jiang1,*, Xiaoliang Wang1, Frank Jiang2
    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 187-197, 2020, DOI:10.32604/jihpp.2020.016243
    Abstract Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods, the movement characteristics of trajectory data cannot be well expressed, which in turn affects the accuracy of the prediction results. First, a new trajectory data expression method by associating the movement behavior information is given. The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region. The movement behavior features based on pre-association may not always be the… More >

  • Open Access

    ARTICLE

    Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

    Qianqian Li1, Meng Li2, Lei Guo3,*, Zhen Zhang4
    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 199-205, 2020, DOI:10.32604/jihpp.2020.016299
    Abstract On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time, complexity and high-difficulty for processing. Therefore, data cleaning is essential for on-site programming big data. Duplicate data detection is an important step in data cleaning, which can save storage resources and enhance data consistency. Due to the insufficiency in traditional Sorted Neighborhood Method (SNM) and the difficulty of high-dimensional data detection, an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed. The efficiency of the algorithm can be elevated by improving the method… More >

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