Recognition of PQ stego images based on identifiable statistical feature

Ji-cang LU; Fen-lin LIU; Xiang-yang LUO; Yi ZHANG

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

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Recognition of PQ stego images based on identifiable statistical feature

Academic paper | 更新时间：2024-06-05

- Recognition of PQ stego images based on identifiable statistical feature
- Journal of Communications Vol. 36, Issue 3, Pages: 139-148(2015)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61272489);The National Natural Science Foundation of China(61379151);The National Natural Science Foundation of China(61302159);China Postdoctoral Sci-ence Foundation(20110491838);China Postdoctoral Sci-ence Foundation(2012T50842);Foundation of Science and Technology on Information Assurance Laboratory(KJ-14-108);The Excellent Youth Foundation of Henan Province of China(14410051001);The Doctoral Dissertation Innovation Fund of Zhengzhou Information Science and Technology Institute(BSLWCX201203)
- DOI：10.11959/j.issn.1000-436x.2015068
  CLC： TP391.4
- Online First：2015-03，
  
  Published：25 March 2015
- 稿件说明：
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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：

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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