臺灣博碩士論文加值系統

本研究提出一種新型的資訊加密技術並應用於數位影像浮水印上，在目標影像遭受破壞或是竄改時，影像能先判斷受破壞或是遭竄改的區域，並嘗試從有限的資訊中修復影像，提供數位影像第一層的保護，之後經由稀疏逼近演算法分析修復後的影像資訊，並以分析結果做為解密浮水印資訊的依據。經實驗結果證明，受損區域高達90%的數位影像經修復後，與原影像的峰值訊號雜訊比約20dB，經解密後的浮水印資訊正確率約70%。 在流程上，本研究可分成影像資訊保護技術與浮水印加密技術兩大部分。在影像資訊保護技術上，採用隱像術的方法做處理，目的是將一張解析度較低的數位影像資訊以最低有效位元的方式藏入其中，確保日後影像遭受竄改破壞時能利用此低解析度資訊還原影像。偽造影像與原數位影像的峰值訊號雜訊比皆能維持在40dB以上，無法單純使用肉眼察覺兩者差異。另外於浮水印加密技術上，將藏有低解析度影像資訊的偽造影像分成大小相同且不重複的數個區塊，並使用編碼簿做對每個區塊執行稀疏逼近的運算，記錄運算結果中擁有最大係數的向量索引值，以此索引值的二進制格雷碼對數位浮水印資訊做加密。 本研究所提出的浮水印資訊保護技術，能有效解決數位浮水印於應用上所產生的資訊損失與浮水印關連兩項問題，提供數位浮水印資訊更強健的保護，在數位影像受到大範圍的資訊竄改與破壞下，皆能有效還原數位浮水印資訊。

This paper provides a novel method for data protection to resolve the issue of mass-tampered regions of watermarks. Our approach involves sparse approximation and follows the frequency domain technique, where a trained codebook exists as a dictionary and a region of image exists as a sparse approximation vector. There are two major components to our study: tamper recovery and watermark encryption. Specifically, we generate self-recovery images by Steganography technique on the basis of our previous research, and to generate encryption information, we mark data with the maximum coefficient resulting from the sparse approximation and weighted with Hamming Code approach. The results demonstrate that each block of image processed by the sparse approximation algorithm has its main texture feature associated with a codeword, it has a higher sparse coefficient value than the other codeword. This provides a robust method to protect data via digital watermarking and to verify the copyright of the image. Our proposed method efficiently resists common image attack, such as image tampering, blurring and compression for transmission via popular communication applications. Under common conditions, our method confirmed an outstanding unit correction rate of approximately 90%, especially for tampered images.

摘 要 i ABSTRACT ii 誌 謝 iii 目 錄 iv 圖目錄 vii 表目錄 x 第一章 緒 論 1 1.1 研究背景 1 1.2 研究動機與目的 4 1.2.1 研究動機 4 1.2.2 研究目的 5 1.3 研究特色與貢獻 7 1.4 論文結構 8 第二章 相關研究 10 2.1 數位浮水印技術 10 2.1.1 空間域 11 2.1.2 頻率域 12 2.2 影像竄改與破壞偵測技術 16 2.3 影像修補技術 19 2.4 稀疏逼近演算法 22 第三章 研究方法 28 3.1 數位影像資訊保護技術 28 3.1.1 產生嵌入資訊 29 3.1.2 資訊嵌入於原影像 31 3.1.3 偽造影像的破壞檢測與修復 34 3.1.4 小結 35 3.2 數位影像浮水印加密技術 36 3.2.1 稀疏逼近演算法 37 3.2.2 數位浮水印加密 43 3.2.3 小結 46 3.3 遭遇困難與解決途徑 47 3.3.1 編碼字典集涵蓋範圍問題 47 3.3.2 影像受破壞區域過大問題 50 3.3.3 稀疏逼近演算法選擇問題 52 3.3.4 影像中目標區域大小選擇問題 55 3.4 總結 58 第四章 實驗結果 59 4.1 實驗環境 59 4.2 影像竄改檢測與修補 61 4.3 數位浮水印資訊保護 64 4.3.1 區域竄改破壞攻擊 65 4.3.2 JPEG壓縮攻擊 70 4.3.3 其他類型的數位影像攻擊 75 第五章 結論與未來展望 80 5.1 結論 80 5.2 未來展望 82 參考文獻 83

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