發(fā)布時(shí)間：2018-05-03 03:33

  本文選題：信息隱藏 + 隱寫分析��； 參考：《中國(guó)民航大學(xué)》2017年碩士論文

【摘要】：隨著信息隱藏技術(shù)的快速發(fā)展,隱寫術(shù)作為其主要分支在網(wǎng)絡(luò)通信中發(fā)揮著越來越重要的作用,但隱寫術(shù)如果被不法分子所利用將會(huì)造成嚴(yán)重的安全隱患。隱寫分析技術(shù)通過檢測(cè)載體中是否含有秘密信息,阻斷非法信息的傳遞,從而對(duì)抗隱寫術(shù)的非法使用,保證通信的安全性。本文以G.723.1語音編碼器為載體,對(duì)基于量化索引調(diào)制(QIM,Quantization Index Modulation)的隱寫方法進(jìn)行檢測(cè)。首先,在研究矢量量化過程中QIM隱寫前后碼字索引值發(fā)生變化的基礎(chǔ)上,分析索引值出現(xiàn)的概率以及相鄰索引值之間的相互影響;然后基于索引值分布特性的變化,通過索引的分布概率矩陣和轉(zhuǎn)移概率矩陣對(duì)其進(jìn)行量化,使用主成分分析法進(jìn)行降維處理,得到維數(shù)較低的特征向量,并通過實(shí)驗(yàn)驗(yàn)證了降維后的向量仍對(duì)QIM隱寫靈敏;最后,將提取的特征向量作為支持向量機(jī)(SVM,Support Vector Machine)分類器的輸入,針對(duì)不同類別的語音樣本進(jìn)行實(shí)驗(yàn),通過大量樣本訓(xùn)練分類器,用以檢測(cè)QIM隱寫。針對(duì)所提方法的準(zhǔn)確性和可靠性進(jìn)行了實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明對(duì)分布概率矩陣和轉(zhuǎn)移概率矩這兩類特征向量,檢測(cè)率均高于90%,虛警率均低于8%,說明該方法準(zhǔn)確性較高;全局檢測(cè)率均在90%以上,說明該方法具有較好的可靠性。針對(duì)不同嵌入率和不同時(shí)長(zhǎng)語音樣本進(jìn)行了實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明語音時(shí)長(zhǎng)在3s以上時(shí),對(duì)分布概率矩陣和轉(zhuǎn)移概率矩這兩類特征向量的檢測(cè)率均達(dá)到85%,而分布概率矩陣對(duì)時(shí)長(zhǎng)變化更加敏感;隨著嵌入率的增長(zhǎng),檢測(cè)準(zhǔn)確率變高。
[Abstract]:With the rapid development of information hiding technology, steganography plays a more and more important role in network communication, but the use of steganography will cause serious security risks if it is used by illegal elements. The illegal use of anti steganography ensures the security of communication. This paper uses G.723.1 speech coder as the carrier to detect the steganography based on quantized index modulation (QIM, Quantization Index Modulation). First, the index value appears on the basis of the change of the index value of the codeword before and after the study of the QIM steganography in the vector quantization process. The probability and the interaction between the adjacent index values, and then quantizing it based on the distribution probability matrix of index and the transfer probability matrix based on the change of the index distribution characteristics, and use the principal component analysis to reduce the dimension of the dimension by using the principal component analysis method, and verify that the vector of the reduced dimension is still QIM hidden through the experiment. In the end, the extracted feature vectors are used as the input of the SVM (Support Vector Machine) classifier. Experiments are carried out for different class of speech samples, and a large number of samples are used to train the classifier to detect the QIM steganography. The accuracy and reliability of the proposed method are tested. The experimental results show that the distribution is generally distributed. The rate matrix and the transfer probability moment are all two kinds of eigenvectors, the detection rate is higher than 90%, the false alarm rate is lower than 8%, which indicates that the method is more accurate and the global detection rate is above 90%. It shows that the method has good reliability. The experiment is carried out for different embedding rates and different simultaneous long speech samples, and the experimental results show that the speech is longer than 3S. The detection rate of two types of eigenvectors, the distribution probability matrix and the transfer probability moment, is up to 85%, while the distribution probability matrix is more sensitive to the change of the length of time, and the detection accuracy becomes higher with the increase of the embedding rate.

【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP309

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