• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2008, Vol. 30 ›› Issue (2): 9-14.

• 论文 • 上一篇    下一篇

基于拉普拉斯模型的序列隐写密钥估计

吴明巧[1,2] 朱中梁[2] 金士尧[1]   

  • 出版日期:2008-02-01 发布日期:2010-05-19

  • Online:2008-02-01 Published:2010-05-19

摘要:

图像序列隐写是指利用载体图像特征数据(包括频域数据、空域数据)连续嵌入信息的隐藏方法。本文提出了一种针对图像扩展频谱序列隐写的密钥估计算法。该方法基于序列分析与突变检测的理论,利用序列概率比累积和检验方法(CUSUM-SPRT)对变化进行检测。考虑图像DCT系数满足拉普拉斯分布,给出了理想平稳拉普拉斯分布信号扩展频谱隐藏密钥估计的模型。采用随机微分方程法(SDE)生成拉氏分布的随机序列进行实验。对于非平稳信号的图像数据,在低信噪比(SNR)下,利用当地最有效序列检测法,给出了拉普拉斯分布的密钥估计模型。实验显示,该方法不但能检测出扩展频谱隐写,估计嵌入密钥,而且比Trivedi的方法更有效。

关键词: 隐写分析 密钥估计 拉普拉斯分布 图像信息隐藏

Abstract:

Image sequential steganography is defined as a class of embedding algorithms that hide messages in the consecutive features (time, spatial or frequency domain) of the host signal. This paper presents a steganalysis method that estimates the secret key used in the sequential steganography of spread s  spectrum embedding. We use the method of cumulative sum based on the sequential probability test (CUMUM-SPRT) to detect the changes, which is used in sequential analysis and abrupt changes detection. Considering the Laplacian distribution of image DCT coefficients, we give a secret key estimation model of stationary Laplacian host signals embedded by the spread spectrum steganography. Experiments are done using the stationary Laplacian random sequence generated by a stochastic differential equation(SDE) method. For non-statioanry digital images with a low signal noise ratio (SNR), a locally mos st powerful steganaysis detector is derived based on the Laplacian distribution. The results of experiments show that our method is more efficient than  Trivedi's method.

Key words: steganalysis;secret key estimation, Laplacian distribution, image information hiding