臺灣博碩士論文加值系統

隨著數位科技的進步，數位媒體資料的散播變得相當容易，使得非法的重製和修改的情況愈來愈嚴重。數位浮水印能夠藏入特定資訊在數位媒體中，並提供對應的機制去保護及識別數位媒體。在本論文中，我們提出了一個植基於逆傳遞類神經網路架構(counterpropagation neural network)的數位影像浮水印技術，透過逆傳遞類神經網路來實現浮水印的嵌入及擷取程序。不同於傳統浮水印的方法，論文中浮水印是記憶在網路的神經鍵(synapses)中，因此在嵌入數位浮水印後的影像品質幾乎不受任何影響，也因為如此，嵌入數位浮水印後的影像遭受攻擊而造成的損壞，亦不會影像擷取出的浮水印品質。此外，透過提出的方法，我們能夠整合浮水印的嵌入及擷取程序至一逆傳遞類神經網路，並且該網路也能夠實現單一浮水印對多張影像和多浮水印對多張影像的嵌入及擷取。實驗結果顯示，透過本論文提出的浮水印方法，具有強韌性、不可查覺性和確定性。

The rapid development of computer network and multimedia technology makes it easier to assess digital media. Since the problem of illegal reproduction and modification has become more serious than before. Digital watermarks are an important technique for protection and identification that allows authentic watermarks to be hidden in multimedia. In this thesis, we propose a novel method called Full Counterpropagation Neural Network (FCPN) for digital image watermarking, in which the watermark is embedded and extracted through specific FCPN. Different from the traditional methods, the different watermarks are embedded in the synapses of a FCPN instead of the cover image. Therefore, the watermarked image is almost the same as the original cover image. In addition, most of the attacks could not degrade the quality of the extracted watermark image. Moreover, the watermark embedding procedure and extracting procedure is integrated into the proposed FCPN and it also accomplishes watermark embedding and extraction in one watermark by multi-cover image or multi- watermark by multi-cover image. The experimental results show that the proposed method is able to achieve robustness, imperceptibility and authenticity in watermarking.

Abstract in Chinese ．．i
Abstract ．．ii
Acknowledgment ．．iv
Contents ．．v
List of Figures ．．vii
List of Tables ．．ix
Chapter 1 Introduction．．1
1.1 Motivation．．1
1.2 Relative Works to Watermarking．．1
1.3 Objectives．．3
1.4 Thesis Outline．．4
Chapter 2
Counterpropagation Neural Network ．．5
2.1 Introduction．．5
2.1.1 Forward-Only Counterpropagation Neural Network．．6
2.1.2 Full Counterpropagation Neural Network．．7
Chapter 3 Full Counterpropagation Neural Network for Watermarking．．10
3.1 Introduction．．10
3.2 Embedding Algorithm．．14
3.3 Extracting Algorithm．．15
Chapter 4 Experimental Results．．16
4.1 Multiple Image Watermarking．．18
4.2 Robustness test．．22
4.2.1 Robustness testing for Gray-Scale Watermark．．23
4.2.2 Robustness testing for Binary Watermark．． 27
4.3 Authenticity test．．32
4.4 Multiple Watermark Watermarking．．33
4.5 Discussions．．35
Chapter 5 Conclusions．．41
References ．．42
Appendix Thesis in Chinese．．44

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