近年來，隨著科技的高度發展，網路已成為生活中不可或缺的一部份，所以我們要更重視網路安全的問題。
在2008年，劉教授提出Complementary Embedding Strategy (CES)，是一種具備高嵌入量的JPEG藏密演算法，此藏密演算法也可以抵禦幾種攻擊，諸如：卡方攻擊和S系列攻擊等。而JPEG影像格式被廣泛應用於網路傳輸和數位相機。鑒於此，我們選擇這方法來做些微的修改，並更進一步提升可嵌入的容量。
在本論文中，我們先複習藏密學的概念，以及幾個現存知名的藏密法。然後，呈現我們提出的方法，並進一步做實驗與討論。由於CES之E1演算法最後的步驟會對qDCT係數做出從-2到1的過度修改，所以我們用影像前處理來取代CES之E1演算法最後步驟的係數修改，來改進藏密影像的PSNR。這部分被稱為修改後的CES。此外，若我們的機密訊息位元數量大於修改後的CES之可嵌入量，在執行完修改的CES後，我們會執行增加嵌入量的方法。這方法可增加令人滿意的嵌入量，而PSNR會稍微降低。我們藉由六張512×512的256灰階影像來測試不同藏密法的PSNR和可嵌入量。

In recent years, the Internet has become an indispensable part of life with the highly developed technology, so we cannot ignore the issue about network security.
In 2008, Liu proposed a JPEG steganographic algorithm of higher capacity, Complementary Embedding Strategy (CES), and this algorithm can resist several steganographic attacks, such as χ2 attack and S family attacks. And JPEG image format is widely used in a digital camera and in the Internet transmission. In view of this, we choose CES to slightly modify and further increase capacity.
In this thesis, we review the concept of steganography and several well-known existing steganographic algorithms first. Then, we present our proposed method, the experimental results and conclusion. Because E1 algorithm of CES may over-modify the selected quantized Discrete Cosine Transform (qDCT) coefficient in the last step to fix the qDCT distribution, we replace the coefficient modification in last step of E1 algorithm of CES with image pre-processing to slightly improve the PSNR value of the stego-image. This section is called modified CES. In addition, if the number of bits of the secret message is larger than the available capacity of modified CES, we will execute a method of raising embedding capacity to increase the available capacity of the cover-image. This method, while it satisfactorily increases the capacity, incurs a slight tradeoff of decreased PSNR value. We test different steganographic algorithms by six 512×512 images with 256 gray levels and show these images’ experimental results of PSNR value and capacity.

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