Special Quantum Steganalysis Algorithm for Quantum Secure Communications Based on Discriminator

被引:0
作者
Liu, Xinzhu [1 ,2 ]
Qu, Zhiguo [1 ,2 ,3 ,4 ]
Chen, Xiubo [4 ]
Wang, Xiaojun [5 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Engn Res Ctr Digital Forens, Minist Educ, Nanjing 210044, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Sch Comp Sci, Nanjing 210044, Peoples R China
[3] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China
[4] Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[5] Dublin City Univ, Sch Elect Engn, Dublin, Ireland
来源
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2023年 / 17卷 / 06期
基金
中国国家自然科学基金;
关键词
Quantum computing; quantum discriminator; quantum machine learning; quantum steganalysis; quantum steganography; STEGANOGRAPHY; PROTOCOL; STRATEGY; PATTERN;
D O I
10.3837/tiis.2023.06.008
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%.
引用
收藏
页码:1674 / 1688
页数:15
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