Home / Journals / JIHPP / Vol.2, No.1, 2020
  • Open Access

    ARTICLE

    Research on Denoising of Cryo-em Images Based on Deep Learning

    Jianquan Ouyang*, Yi He, Huanrong Tang, Zhousong Fu
    Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 1-9, 2020, DOI:10.32604/jihpp.2020.010657
    Abstract Cryo-em (Cryogenic electron microscopy) is a technology this can build bio-macromolecule of three-dimensional structure. Under the condition of now, the projection image of the biological macromolecule which is collected by the Cryo-em technology that the contrast is low, the signal to noise is low, image blurring, and not easy to distinguish single particle from background, the corresponding processing technology is lagging behind. Therefore, make Cryoem image denoising useful, and maintaining bio-macromolecule of contour or signal of function-construct improve Cryo-em image quality or resolution of Cryo-em three-dimensional structure have important effect. This paper researched a denoising function base on GANs (generative… More >

  • Open Access

    ARTICLE

    Research on Prevention of Citrus Anthracnose Based on Image Retrieval Technology

    Xuefei Du*, Xuyu Xiang
    Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 11-19, 2020, DOI:10.32604/jihpp.2020.010114
    Abstract Citrus anthracnose is a common fungal disease in citrus-growing areas in China, which causes very serious damage. At present, the manual management method is time-consuming and labor-consuming, which reduces the control effect of citrus anthracnose. Therefore, by designing and running the image retrieval system of citrus anthracnose, the automatic recognition and analysis of citrus anthracnose control were realized, and the control effect of citrus anthracnose was improved. In this paper, based on the self-collected and collated citrus anthracnose image database, we use three image features to realize an image retrieval system based on citrus anthracnose through SMV, AP clustering optimization.… More >

  • Open Access

    REVIEW

    A Survey on Face Anti-Spoofing Algorithms

    Meigui Zhang*, Kehui Zeng, Jinwei Wang
    Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 21-34, 2020, DOI:10.32604/jihpp.2020.010467
    Abstract The development of artificial intelligence makes the application of face recognition more and more extensive, which also leads to the security of face recognition technology increasingly prominent. How to design a face anti-spoofing method with high accuracy, strong generalization ability and meeting practical needs is the focus of current research. This paper introduces the research progress of face anti-spoofing algorithm, and divides the existing face anti-spoofing methods into two categories: methods based on manual feature expression and methods based on deep learning. Then, the typical algorithms included in them are classified twice, and the basic ideas, advantages and disadvantages of… More >

  • Open Access

    ARTICLE

    Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

    Zaoyu Wei1,*, Jiaqi Wang2, Xueqi Shen1, Qun Luo1
    Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 35-45, 2020, DOI:10.32604/jihpp.2020.010331
    Abstract Smart contract has greatly improved the services and capabilities of blockchain, but it has become the weakest link of blockchain security because of its code nature. Therefore, efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system. Oriented to Ethereum smart contract, the study solves the problems of redundant input and low coverage in the smart contract fuzz. In this paper, a taint analysis method based on EVM is proposed to reduce the invalid input, a dangerous operation database is designed to identify the dangerous input, and genetic algorithm is used to optimize the… More >

  • Open Access

    REVIEW

    A Survey on Adversarial Example

    Jiawei Zhang*, Jinwei Wang
    Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 47-57, 2020, DOI:10.32604/jihpp.2020.010462
    Abstract In recent years, deep learning has become a hotspot and core method in the field of machine learning. In the field of machine vision, deep learning has excellent performance in feature extraction and feature representation, making it widely used in directions such as self-driving cars and face recognition. Although deep learning can solve large-scale complex problems very well, the latest research shows that the deep learning network model is very vulnerable to the adversarial attack. Add a weak perturbation to the original input will lead to the wrong output of the neural network, but for the human eye, the difference… More >

Share Link

WeChat scan