基于辨识性统计特征的PQ隐密图像识别算法

卢记仓; 刘粉林; 罗向阳; 张轶

doi:10.11959/j.issn.1000-436x.2015068

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基于辨识性统计特征的PQ隐密图像识别算法

学术论文 | 更新时间：2024-06-05

- 基于辨识性统计特征的PQ隐密图像识别算法
- Recognition of PQ stego images based on identifiable statistical feature
- 通信学报 2015年36卷第3期页码：139-148
- 作者机构：
  
  1. 解放军信息工程大学导航与空天目标工程学院，河南郑州 450001
  2. 解放军信息工程大学网络空间安全学院，河南郑州 450001
  3. 数学工程与先进计算国家重点实验室，河南郑州 450001
  4. 中国科学院信息工程研究所信息安全国家重点实验室，北京 100093
- 作者简介：
  
  [ "卢记仓（1985-），男，河北大名人，解放军信息工程大学讲师，主要研究方向为信息隐藏与检测。" ]
  [ "刘粉林（1964-），男，江苏溧阳人，解放军信息工程大学教授、博士生导师，主要研究方向为网络信息安全、信息隐藏与检测。" ]
  [ "罗向阳（1978-），男，湖北荆门人，解放军信息工程大学副教授、博士生导师，主要研究方向为网络信息安全、信息隐藏与检测。" ]
  [ "张轶（1987-），男，河南漯河人，解放军信息工程大学博士生，主要研究方向为信息隐藏与检测。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61272489);国家自然科学基金资助项目(61379151);国家自然科学基金资助项目(61302159);中国博士后科学基金资助项目(20110491838);中国博士后科学基金资助项目(2012T50842);信息保障技术重点实验室开放基金资助项目(KJ-14-108);河南省科技创新杰出青年基金资助项目(14410051001);解放军信息工程大学优博基金资助项目(BSLWCX201203)
- DOI：10.11959/j.issn.1000-436x.2015068
  中图分类号： TP391.4
- 网络出版日期：2015-03，
  
  纸质出版日期：2015-03-25
- 稿件说明：
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卢记仓, 刘粉林, 罗向阳, 等. 基于辨识性统计特征的PQ隐密图像识别算法[J]. 通信学报, 2015,36(3):139-148.

Ji-cang LU, Fen-lin LIU, Xiang-yang LUO, et al. Recognition of PQ stego images based on identifiable statistical feature[J]. Journal of communications, 2015, 36(3): 139-148.
卢记仓, 刘粉林, 罗向阳, 等. 基于辨识性统计特征的PQ隐密图像识别算法[J]. 通信学报, 2015,36(3):139-148. DOI： 10.11959/j.issn.1000-436x.2015068.

Ji-cang LU, Fen-lin LIU, Xiang-yang LUO, et al. Recognition of PQ stego images based on identifiable statistical feature[J]. Journal of communications, 2015, 36(3): 139-148. DOI： 10.11959/j.issn.1000-436x.2015068.

摘要

提出一种基于辨识性统计特征的PQ（perturbed quantization）隐密图像识别算法。该算法根据经典PQ隐写对图像数据的更改方式，提取可有效区分该类隐密图像与其他类隐密图像的辨识性统计特征，并运用SVM（support vector machines）分类器进行分类识别。实验结果表明，本算法能够可靠地将PQ隐密图像从5类典型JPEG隐写PQ、F5、nsF5、MB1和MOD的隐密图像中识别出来；即使F5、nsF5、MB1和MOD的隐密图像不参与分类器的训练，本算法仍能有效识别PQ隐密图像。

Abstract

A PQ (perturbed quantization) stego images recognition algorithm is proposed based on identifiable statistical feature. According to the specific changing ways of PQ steganography to image data

the proposed algorithm extracts the identifiable statistical feature that can distinguish PQ stego images from other types of stego images. Then

the SVM (support vector machines) classifier is trained to recognize PQ stego images. Experimental results show that

the proposed algorithm can reliably recognize PQ stego images from multi-class stego images generated by five types of well-known JPEG steganography (PQ、F5ns、F5、MB1 and MOD). Even though the stego images generated by F5、nsF5、MB1 and MOD are not used for training classifier

the proposed algorithm can still effectively recognize PQ stego images.

关键词

Keywords

references

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