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研究生:蔡沛緯
研究生(外文):Pei-Wei Tsai
論文名稱:使用軟性計算輔助資訊隱藏演算法之研究
論文名稱(外文):Soft Computing for Information Hiding
指導教授:潘正祥朱淑娟朱淑娟引用關係
指導教授(外文):Jeng-Shyang PanShu-Chuan Chu
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子與資訊工程研究所碩士班
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:2007
畢業學年度:95
語文別:英文
論文頁數:86
中文關鍵詞:貓演算法最佳化軟計算資訊隱藏浮水印
外文關鍵詞:Cat Swarm OptimizationCSOSoft ComputingOptimizationInformation HidingWatermarking
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本研究基於取法自然界生物之求生本能和其行為模式以貓科動物作為觀察與取法之對象,並藉由建構其行為模式之模型模擬其種種特性,並將這些特性應用於求解最佳化問題上。取法於自然界生物行為以及生物演化之最佳化演算已被大量應用在求解最佳化問題上,其中較為廣泛被應用的演算法有基因演算法、粒子聚積法,以及螞蟻演算法等。
本研究所提出之演算法,在解空間上的移動分為兩個模式,其一是追蹤模式,其二則是探索模式。藉由這兩種模式之適當切換便能將貓科動物在追蹤移動物和休息時的特性表現出來,進而將一組解自解空間的某一處移動至另一個位置,並達到漸進式求解之效果。此演算法在建構完成後於實驗時與粒子聚積法相互比較,透過實驗模擬所顯示之結果證明,本研究所提出之「貓演算法」確實能有更有效的求得最佳解。
於建構出完整最佳化演算法模型後,本研究係將前述之「貓演算法」實際應用於資訊隱藏當中。由於資訊隱藏演算法會因為所選擇之資訊嵌入點不同,而導致其嵌入資料後資訊載體之特性及資訊可嵌入容量產生不同之變化,故於處理資訊隱藏時,經常需於上述考慮因素間權衡,此時最佳化演算法則能發揮協助評估且兼具考慮因素之功效。於此應用之測試結果中,本研究所提出之最佳化演算法確能適時發揮有效之最佳解搜尋效果,並對於所應用之資訊隱藏之資訊嵌入點選擇產生良性之影響。
An innovative scheme for optimization algorithm based on observing and imitating creatures’ constitutional behaviors is proposed in this thesis. Taking behaviors from natural organisms to form the optimization algorithm is an effective way for solving optimization problems.
The main purpose of this thesis is to establish an optimization algorithm by means of modeling the observed congenital behaviors from the specific species, cat, for solving optimization problems. We present two sub-models, the tracing mode and the seeking mode, for moving the solution sets from one position to another on the solution space. By properly allocating these two sub-models in the evolution, we imitate the behaviors of tracing moving objects and the resting of the cat. Moreover, when applying these sub-models into the algorithm, the solution sets are able to move from one position to a new position. Then, the evolutionary algorithm, Cat Swarm Optimization (CSO), for optimization is achieved.
In order to investigate the performance of CSO, we compare CSO with one existing technique called Particle Swarm Optimization (PSO) in the experiments by testing several functions. According to the experimental results, as we expected, CSO achieves searching of the global optimum more swiftly and more precisely than PSO does.
Furthermore, we apply CSO into information hiding. When increasing the robustness of the hidden information, the quality of the media containing hidden information becomes degraded. On the contrary, the robustness of the hidden information becomes weaker while the cover media perform more similarly to the original media. Balancing between the robustness of the hidden information and the similarity of the cover media is a frequent trade-off problem in information hiding, and the optimization algorithm is very useful for solving this kind of problem. Based on the results, the application of CSO and the optimization algorithm proposed in this thesis are able to obtain the best search results while producing positive influences on the hidden information at the same time. Without doubt, the proper decision can be found with the application of CSO.
CHINESE ABSTRACT I
ABSTRACT II
ACKNOWLEDGEMENT III
CONTENTS IV
TABLE CONTENTS VII
FIGURE CONTENTS VIII
CHAPTER 1. INTRODUCTION AND OVERVIEW 1
1.1 PREFACE 1
1.2 AIMS AND GOALS OF OUR RESEARCH 2
1.3 OVERVIEW OF THE THESIS 2
CHAPTER 2. OPTIMIZATION IN SOFT COMPUTING 4
2.1 CONCEPT OF OPTIMIZATION IN SOFT COMPUTING 4
2.2 GENETIC ALGORITHM 5
2.2.1 Coding Scheme 5
2.2.2 Parameters Setting for GA 8
2.2.3 Initialization 10
2.2.4 Evaluation 11
2.2.5 Selection 11
2.2.6 Crossover 13
2.2.7 Mutation 15
2.2.8 Termination Checking 16
2.3 PARTICLE SWARM OPTIMIZATION AND PSO WITH WEIGHTING FACTOR 16
2.3.1 The Coding Scheme and Parameter Settings 17
2.3.2 Particle Initialization 19
2.3.3 Velocity Updating 19
2.3.4 Particle Position Updating and Memory Updating 20
2.3.5 Termination Checking 21
2.4 SIMULATED ANNEALING 22
2.4.1 Parameter Initialization 23
2.4.2 Solution Generating 23
2.4.3 Solution Updating 23
2.4.4 Temperate Decreasing 24
2.4.5 Termination Checking 24
CHAPTER 3. INFORMATION SECURITY 25
3.1 PERCEPTION OF INFORMATION SECURITY 25
3.2 STEGANOGRAPHY 27
3.3 WATERMARKING TECHNIQUES 27
3.4 ASSOCIATED SCHEMES FOR INFORMATION HIDING 31
CHAPTER 4. DISCRETE WAVELET TRANSFORM BASED
WATERMARKING SCHEME 33
4.1 DISCRETE WAVELET TRANSFORM (DWT) 33
4.2.1 Watermark Embedding 37
4.3 MESSAGE EXTRACTION OF DWT BASED INFORMATION HIDING SCHEME 41
CHAPTER 5. DESIGN OF CAT SWARM OPTIMIZATION 42
5.1 ABSTRACTION OF CAT SWARM OPTIMIZATION 42
5.2 OBSERVING CREATURES’ BEHAVIORS 42
5.2.1 Scrutinizing Motions of Creatures 43
5.2.2 Inspecting the Behaviors of Felids 43
5.3 STRUCTURE OF CAT SWARM OPTIMIZATION 44
5.3.1 The Presentation of Solution Sets 45
5.3.2 Rest and Alert -- Seeking Mode 45
5.3.3 Movement -- Tracing Mode 47
5.4 CORE DESCRIPTION OF CAT SWARM OPTIMIZATION 47
5.5 EXPERIMENTS AND EXPERIMENTAL RESULTS 48
5.5.1 Test Functions 49
5.5.2 Parameter Settings for CSO, PSO and PSO with WF 51
5.5.3 Experimental Results 56
5.6 DISCUSSION 56
5.7 CONCLUSION 56
CHAPTER 6. CSO FOR INFORMATION HIDING BASED ON DWT 58
6.1 CONCEPT OF THE EXPERIMENT 58
6.2 OUTCOME OF THE ORIGINAL DWT BASED WATERMARKING TECHNIQUE 58
6.3 THE ROLE OF CSO IN DWT BASED WATERMARKING SCHEME 61
6.4 EXPERIMENTS AND EXPERIMENTAL RESULTS 62
6.5 CONCLUSION 67
CHAPTER 7. SUMMARY 68
7.1 CONCLUSION OF THE THESIS 68
7.2 FUTURE DIRECTIONS 68
REFERENCE 69
PUBLICATION LIST DURING THE MASTER STUDY. 72
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