JPEG steganalysis based on Tri-training semi-supervised learning

GUO Yan-qing 1; KONG Xiang-wei1; YOU Xin-gang1; HE De-quan1

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JPEG steganalysis based on Tri-training semi-supervised learning

更新时间：2024-10-14

- JPEG steganalysis based on Tri-training semi-supervised learning
- Issue 10, Pages: 205-209(2008)
- 作者机构：
  
  1. 大连理工大学信息安全研究中心
  2. 北京电子技术应用研究所
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN918.91
- Published：2008
- 稿件说明：
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GUO Yan-qing 1, KONG Xiang-wei1, YOU Xin-gang1, et al. JPEG steganalysis based on Tri-training semi-supervised learning[J]. 2008, (10): 205-209.
DOI：

GUO Yan-qing 1, KONG Xiang-wei1, YOU Xin-gang1, et al. JPEG steganalysis based on Tri-training semi-supervised learning[J]. 2008, (10): 205-209. DOI：

摘要

提出了一种基于半监督学习机制的JPEG隐密分析方法。通过三类DCT域统计特征和多超球面OC-SVM算法构建三种独立的隐密分析方法

并以Tri-training学习方式迭代地对未标记图像样本进行标记

来扩充原训练样本集

进而可以利用大量未标记属性的图像样本提高隐密分析算法的泛化能力。由JSteg、F5、Outguess、MB1含密图像与载体图像所组成的混合图像库上的仿真实验结果验证了此方法的有效性。

Abstract

A JPEG steganalytic method based on semi-supervised learning algorithm was presented.Using three catego-ries of statistical features for JPEG images and multiple hyperspheres one-class SVM

three classifiers were generated from the original labeled example set.These classifiers were then refined using unlabeled examples in the Tri-training process

which could effectively improve detecting ability by exploiting a large amount of unlabeled images.Experimen-tal results showed the effectiveness of our proposed method.

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