基于多预训练模型的在线隐写盲分析系统研究与实现

袁亚飞; 卢伟; 冯丙文; 翁健

doi:10.11959/j.issn.2096-109x.2017.00164

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基于多预训练模型的在线隐写盲分析系统研究与实现

学术论文 | 更新时间：2024-06-03

- 基于多预训练模型的在线隐写盲分析系统研究与实现
- Online universal steganalysis system based on multiple pre-trained model
- 网络与信息安全学报 2017年3卷第5期页码：32-37
- 作者机构：
  
  1. 中山大学数据科学与计算机学院，广东广州 510006
  2. 暨南大学信息科学技术学院，广东广州 510632
- 作者简介：
  
  [ "袁亚飞（1991-），男，河南商丘人，中山大学硕士生，主要研究方向为多媒体信息安全与数字取证。" ]
  [ "卢伟（1979-），男，河南南阳人，中山大学副教授、硕士生导师，主要研究方向为多媒体信息安全与数字取证。" ]
  [ "冯丙文（1985-），男，山东东营人，博士，暨南大学讲师，主要研究方向为多媒体安全与数字取证。" ]
  [ "翁健（1976-），男，广东茂名人，博士，暨南大学教授、博士生导师，主要研究方向为密码学与信息安全。" ]
- 基金信息：
  
  广东省科技发展专项基金资助项目;The Special Funds for Science and Technology Development of Guangdong Province(2016KZ010103);广东省自然科学基金资助项目;The Natural Science Foundation of Guangdong Province(2016A030313350);中央高校基本科研业务费基金资助项目;The Fundamental Research Funds for the Central Universities(16lgjc83);中山大学科技成果转化计划基金资助项目;The Scientific and Technological Achievements Transformation Plan of Sun Yat-sen University
- DOI：10.11959/j.issn.2096-109x.2017.00164
  中图分类号： TP393
- 网络首发：2017-05，
  
  纸质出版：2017-05-15
- 稿件说明：
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袁亚飞, 卢伟, 冯丙文, 等. 基于多预训练模型的在线隐写盲分析系统研究与实现[J]. 网络与信息安全学报, 2017,3(5):32-37.

Ya-fei YUAN, Wei LU, Bing-wen FENG, et al. Online universal steganalysis system based on multiple pre-trained model[J]. Chinese Journal of Network and Information Security, 2017, 3(5): 32-37.
袁亚飞, 卢伟, 冯丙文, 等. 基于多预训练模型的在线隐写盲分析系统研究与实现[J]. 网络与信息安全学报, 2017,3(5):32-37. DOI： 10.11959/j.issn.2096-109x.2017.00164.

Ya-fei YUAN, Wei LU, Bing-wen FENG, et al. Online universal steganalysis system based on multiple pre-trained model[J]. Chinese Journal of Network and Information Security, 2017, 3(5): 32-37. DOI： 10.11959/j.issn.2096-109x.2017.00164.

摘要

在实际应用中，针对未知隐写算法的盲检测难度非常大，结合实际应用设计实现了一个在线盲检测系统。在SRM算法的基础上，简化特征提取算法，提高特征可用性和提取速率；使用多个预训练检测模型，并采用加权投票策略判定检测结果；设计实现一种3层系统架构，分布式后台更加灵活高效；为了进一步满足实际应用要求，引入多线程技术，加快检测速率。实验表明，单张图片的平均检测时间可达0.97 s，并且对多种未知隐写算法均具有良好的检测结果。

Abstract

In reality

universal blind steganalysis is still a sensitive issue.A universal online steganalysis system that could be used in practical application was proposed.With reducing the dimensions of SRM

it could improve availability and speed up feature extraction.Some effective pre-trained models and weighted voting strategy were used in this system with a B/S architecture

involving a higher speed.In addition

multithread technology was introduced.Experimental results demonstrate that high detection accuracy can be obtained and about 0.97 seconds for single detection with the system.

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Keywords

references

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