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研究生:許富翔
研究生(外文):Fu-Hsiang Hsu
論文名稱:使用權重模數之影像偽裝學研究
論文名稱(外文):A Study of Image Steganography Using Weighted Modulus
指導教授:王宗銘王宗銘引用關係
口試委員:張忠賢蔡淵裕
口試日期:2013-01-09
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊科學與工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:117
中文關鍵詞:權重模數高藏量高影像品質資訊隱藏低動態範圍影像高動態範圍影像直方圖調整方法影像偽裝學
外文關鍵詞:weighted modulushigh capacity and high qualitydata hidinglow dynamic range imagehigh dynamic range imagehistogram adjustment methodimage steganography
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本篇論文提出兩個基於權重模數的高藏量高影像品質資訊偽裝演算法。針對低動態範圍的灰階與彩色影像,我們提出一個利用權重模數(Weighted Modulus, WM)來達成高藏量高品質之影像偽裝演算法,簡稱WM方法。我們先使用理論分析求出在不同像素位元嵌入量下,偽裝影像具有的理論最小變動量。接著,提出如何在不同像素位元嵌入量下,尋找最佳權重的方法。使用最佳權重來嵌入秘密訊息時,使符合或盡可能的相近於理論最小變動量。我們的演算法使用有效或最佳權重來嵌入秘密訊息,產生高視覺品質的偽裝影像。理論分析結果顯示:在相同的嵌入量下,WM演算法可提高影像的峰值訊噪比 (Peak Signal to Noise Ratio, PSNR)數值。實驗結果也佐證演算法產生之偽裝影像品質優於目前文獻上現有的高藏量高品質資訊偽裝演算法。WM方法周延可以使用多個像素來嵌入密訊息,同時也具有高度彈性,可以使用線性內插滿足使用者需求。演算法具有抵抗RS偽裝偵測攻擊之安全性。
我們提出的第二個演算法適用於高動態範圍影像,簡稱WMHDR方法。此演算法以高動態範圍影像OpenEXR格式為承載媒體,根據其特性,擴展權重模數,來達成高藏量高品質之影像偽裝。OpenEXR格式是由符號位元、指數、尾數部分組成的浮點數儲存格式。由於尾數部分是由10個位元來儲存,比低動態範圍影像的8個位元多,因此我們擴展權重範圍,將秘密訊息嵌入至尾數部分,促使我們的演算法能在侷限的影像變動前提下,提供更高的單位嵌入量。此外,若承載媒體之尾數部分存在大量的邊界像素,在嵌入秘密訊息時,將導致以尾數建構之直方圖,產生異常突起,引發偽裝偵測之疑慮。據此,我們提出了一個直方圖調整方法(Histogram Adjustment Method, HAM)來修正此異常。HAM演算法藉由分析直方圖之異常情況,提出因應之策略來消除直方圖之異常突起。再來,我們對偽裝影像進行視覺化差異評估與量化量測。實驗結果顯示:我們的WMHDR演算法可以嵌入更多的秘密訊息。偽裝高動態範圍影像經過色調映射後,仍能維持相當高的影像品質。我們提出的HAM直方圖調整方法,能有效的修正尾數直方圖異常突起,使之更接近於原始掩護影像,增加抗偽裝偵測之能力。視覺化差異評估結果顯示,偽裝與掩護影像間具有極小的視覺差異,僅有極低之機率能被人眼察覺箇中之差異。最後,WMHDR演算法具有相當的安全性,經分析顯示被成功解譯出秘密訊息的機率極低。
總結本研究,我們基於權重模數提出了WM與WMHDR資訊偽裝演算法。WM演算法適用於低動態範圍灰階、彩色影像,具有高藏量低失真、周延、彈性、安全等特性,優於我們所知目前文獻最新的灰階影像資訊隱藏演算法。WMHDR為OpenEXR高動態範圍影像之文獻首創,能提供高藏量、低失真、高安全性之資訊偽裝效益。實驗結果證實演算法效果卓越,擴展影像偽裝學的應用範疇。


We propose two steganographic algorithms of high capacity and high quality which use a weighted modulus technique and extend the feasibility of image-based steganographic applications.
The first algorithm, Weighted Modulus (WM), utilizes a low dynamic range image as a cover image for message embedding. We present a theoretical analysis in order to reveal the minimal pixel variations incurred when concealing different numbers of the message. Then, we introduce an approach that determines the optimal weights which produce the pixel variations equivalent to the theoretical analysis. When optimal weights are not available, we determine effective weights instead. Once a user requests a desired capacity for message embedding, our algorithm employs optimal or effective weights which embed secret messages and produce a stego image with the highest image quality. In comparison with our counterpart, the theoretical analysis shows that our scheme is able to produce a higher PSNR (Peak Signal to Noise Ratio) value when embedding the same number of secret messages. Experimental results verify that our scheme offers a better performance than the counterparts. Our method is general enough to employ three or four pixels as a group in order to embed secret messages. By using a linear combination approach, the WM algorithm can satisfy the capacity demand of the user. The scheme is secured against RS steganalysis attack.
The second algorithm, WMHDR, utilizes a high dynamic range (HDR) image as a cover image. The HDR images of interest are encoded by the OpenEXR format where three data fields, including the sign, exponent and mantissa, are used to record the pixel values. This scheme takes advantage of the mantissa field into which a number of secret messages can be embedded through the embedding weights we have determined. Since the mantissa field of this type of HDR image usually contains a large number of boundary values, this method will produce an abnormal histogram protuberance when embedding secret messages. We tackle this problem by developing a histogram adjustment method (HAM). Experimental results demonstrate that WMHDR can convey a large number of secret messages, while the visual difference detection shows that there is insignificant visual difference between the cover and stego images. The HAM method is effective for alleviating the abnormal phenomenon existing in the histogram of the stego image. When high dynamic range stego images are tone mapped for display, the stego image demonstrates a high image quality where the PSNR value is greater than 30 dB. Finally, theoretical analysis indicates that our scheme is secured, and there is only a small probability that an eavesdropper will be able to break down our scheme and decode the secret messages.
In conclusion, the major contribution of our algorithms is to provide high embedding capacity and low distortion characteristics which conceal secret messages. The cover images that our methods can support include grayscale, low, and high dynamic range images. Our algorithms extend the feasibility of image-based steganographic applications.


目 次
誌 謝 i
摘 要 ii
Abstract iv
目 次 vi
圖 目 次 ix
表 目 次 xi

第一章 緒論 1
1.1 研究動機 1
1.2 研究目標 4
1.3 論文架構 6

第二章 相關文獻探討 7
2.1 各種使用權重模數的方法 7
2.2 FEMD方法 9
2.3 高動態範圍影像OpenEXR格式 13

第三章 利用權重模數的資訊隱藏演算法 15
3.1 理論最小變動量分析 15
3.2 權重分類與尋找 24
3.3 WM演算法秘密訊息之嵌入與取出 29
3.4 WM方法與FEMD之理論變動量分析 33
3.5 實驗結果與比較 39
3.5.1 WM與FEMD實驗結果比較 40
3.5.2 WM方法之擴展彈性 42
3.5.3 滿足使用者需求結果 46
3.5.4 抗偽裝偵測與安全性 49
3.5.5 彩色影像數據 52
3.6 結論 55

第四章 高動態範圍影像權重模數資訊隱藏演算法 57
4.1 訊息嵌入之影響分析 57
4.2 演算法之嵌入與擷取流程 62
4.2.1 基本嵌入模式 63
4.2.2 增加安全性考量模式 66
4.3 直方圖調整方法 68
4.3.1 尾數直方圖異常分析 68
4.3.2 尾數直方圖調整方法 70
4.4 實驗結果與分析 74
4.4.1 本章提出的演算法之實驗數據 76
4.4.2 WMHDR演算法在各種條件下之安全性分析 85
4.4.3比較使用HAM與未使用HAM之結果 88
4.5 結論 94

第五章 總結與未來工作 96
5.1 總結 96
5.2 未來工作 99

參考文獻 100
中英對照表 104
英中對照表 108
附錄A 112
附錄B 116
附錄C 117


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