SIMULATION MODELLING OF ARTIFICIAL NEURAL NETWORKS FOR THE PURPOSES OF STEGANALYSIS

被引:0
作者
Ivanova, Yoana [1 ]
机构
[1] New Bulgarian Univ, Dept Telecommun, Sofia, Bulgaria
来源
INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY | 2022年 / 14卷 / 02期
关键词
steganography; steganalysis; histogram equalization; LSB; Virtual Steganographic Laboratory; Artificial Neural Networks;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper is considered to be a continuation of previous publications devoted to applications of simulation modelling in cybersecurity and in particular the use of Artificial Neural Networks (ANNs) with Backpropagation of Error for digital recognition (IJITS, No4, 2021). It aims to present advanced steganographic methods that are applicable for the purposes of steganalysis to ensure the information protection. The empirical study and the experimental analysis made contribute to explaining various steganographic approaches that are applicable in security.
引用
收藏
页码:99 / 110
页数:12
相关论文
共 50 条
  • [41] STEG-XAI: explainable steganalysis in images using neural networks
    Kuchumova, Eugenia
    Martinez-Monterrubio, Sergio Mauricio
    Recio-Garcia, Juan A.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (17) : 50601 - 50618
  • [42] A New Approach in Financial Modelling with the Aid of Artificial Neural Networks
    Christakis, Nicholas
    Barbaris, Vasileios
    Spentzos, Agis
    JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, 2011, 5 (03) : 513 - 529
  • [43] Modelling the Torque with Artificial Neural Networks on a Tunnel Boring Machine
    Cachim, Paulo
    Bezuijen, Adam
    KSCE JOURNAL OF CIVIL ENGINEERING, 2019, 23 (10) : 4529 - 4537
  • [44] Convolutional Neural Networks for Steganalysis via Transfer Learning
    Tian, Juan
    Li, Yingxiang
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2019, 33 (02)
  • [45] Steganalysis of Neural Networks Based on Symmetric Histogram Distribution
    Tang, Xiong
    Wang, Zichi
    Zhang, Xinpeng
    SYMMETRY-BASEL, 2023, 15 (05):
  • [46] Deep learning for steganalysis via convolutional neural networks
    Qian, Yinlong
    Dong, Jing
    Wang, Wei
    Tan, Tieniu
    MEDIA WATERMARKING, SECURITY, AND FORENSICS 2015, 2015, 9409
  • [47] Modeling and Simulation of Biomass Drying Using Artificial Neural Networks
    Francik, Slawomir
    Lapczynska-Kordon, Boguslawa
    Francik, Renata
    Wojcik, Artur
    RENEWABLE ENERGY SOURCES: ENGINEERING, TECHNOLOGY, INNOVATION, 2018, : 571 - 581
  • [48] Bayesian updating with subset simulation using artificial neural networks
    Giovanis, Dimitris G.
    Papaioannou, Iason
    Straub, Daniel
    Papadopoulos, Vissarion
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2017, 319 : 124 - 145
  • [49] ARTIFICIAL NEURAL NETWORKS USED FOR THE SIMULATION OF THE BATCH FERMENTATION BIOREACTOR
    Cristea, Vasile Mircea
    Arpad, Imre Lucaci
    Anca, Sipos
    Dorina, Bratfalean
    Agachi, Paul Serban
    STUDIA UNIVERSITATIS BABES-BOLYAI CHEMIA, 2009, 54 (04): : 87 - 94
  • [50] Data Fusion and Artificial Neural Networks for Modelling Crop Disease Severity
    Shankar, Priyamvada
    Johnen, Andreas
    Liwicki, Marcus
    PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020), 2020, : 919 - 926