[1]卢璥,伏容,田晖.密钥驱动下多语义维度结构化成绩表生成式隐写方法[J].华侨大学学报(自然科学版),2025,46(3):296-307.[doi:10.11830/ISSN.1000-5013.202504041]
　LU Jing,FU Rong,TIAN Hui.Key-Driven Multi-Semantic Dimensional Generative Steganography for Structured Academic Transcripts[J].Journal of Huaqiao University(Natural Science),2025,46(3):296-307.[doi:10.11830/ISSN.1000-5013.202504041]
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密钥驱动下多语义维度结构化成绩表生成式隐写方法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第46卷
期数:
2025年第3期
页码:
296-307
栏目:
出版日期:
2025-05-20

文章信息/Info

Title:
Key-Driven Multi-Semantic Dimensional Generative Steganography for Structured Academic Transcripts
文章编号:
1000-5013(2025)03-0296-12
作者:
卢璥1; 2;  伏容1; 2;  田晖1; 2
1. 华侨大学 计算机科学与技术学院, 福建 厦门 361021; 2. 厦门市数据安全与区块链技术重点实验室, 福建 厦门 361021
Author(s):
LU Jing1; 2;  FU Rong1; 2;  TIAN Hui1; 2
1. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China; 2. Xiamen Key Laboratory of Data Security and Blockchain Technology, Xiamen 361021, China
关键词:
生成式隐写;  结构化成绩表;  多语义维度;  密钥驱动嵌入
Keywords:
generative steganography;  structured academic transcript;  multidimensional semantics;  key-driven embedding
分类号:
TP309.2
DOI:
10.11830/ISSN.1000-5013.202504041
文献标志码:
A
摘要:
针对教育结构化数据隐写中修改痕迹显著、多语义维度协同编码不足及安全可控性薄弱的问题,提出一种基于密钥驱动与多字段协同生成的无载体隐写方法。该方法以结构化成绩表为隐写对象,融合姓氏、名字、性别、成绩等多语义维度信息,通过主密钥分层派生子密钥,实现高保真度的隐写成绩表生成与可控嵌入。实验结果表明:隐写生成的姓名符合自然命名规范,课程成绩分布特征与基础表高度一致,统计特性稳定且无系统性偏差。与传统方法相比,文中方法在不可感知性、数据保真度与隐蔽性方面更具优势,为教育数据隐写提供了一种兼顾容量与安全性的可靠方案。
Abstract:
Aiming at the issues of significant modification traces, insufficient collaborative encoding of multi-semantic dimensional fields, and weak security controllability in educational structured data steganography, this paper presents a key-driven and multi-semantic dimensional generation-based carrierless steganography method. Using a structured academic transcript as the object for steganography, the method integrates multiple semantic dimensions, including surnames, given names, gender, and grades. It achieves high-fidelity generation and controllable embedding of steganographic academic transcripts by hierarchically deriving sub-keys from the master key. Experimental results show that the generated names conform to natural naming conventions, the grade distribution characteristics remain highly consistent with the original sheet, and the statistical properties are stable with no systematic bias. Compared with traditional methods, this approach demonstrates significant advantages in imperceptibility, data fidelity, and concealment, providing a reliable solution for educational data steganography that balances capacity and security.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2025-04-22
通信作者: 田晖(1982-),男,教授,博士,博士生导师,主要从事网络与信息安全、信息隐藏及其检测和数据安全等的研究。E-mail:htian@hqu.edu.cn。
基金项目: 国家自然科学基金资助项目(61972168, U1536115)
更新日期/Last Update: 2025-05-20