Reversible Information Hiding Techniques for Medical Images

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：36

、訪客IP：3.137.169.186

姓名

吳敏豪(Min-Hao Wu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Reversible Information Hiding Techniques for Medical Images)

相關論文

★ USB WORM KILLER: Cure USB Flash Worms Through a USB Flash Worm	★ Discoverer- Rootkit即時偵測系統
★ 一項Android手機上詐騙簡訊的偵測與防禦機制	★ SRA系統防禦ARP欺騙劫持路由器
★ A Solution for Detecting and Defending ARP Spoofing on Virtual Machines	★ 針對遠端緩衝區溢位攻擊之自動化即時反擊系統
★ 即時血清系統: 具攻性防壁之自動化蠕蟲治癒系統	★ DNSPD: Entrap Botnets Through DNS Cache Poisoning Detection
★ TransSQL: A Translation and Validation-based Solution for SQL-Injection Attacks	★ A Spam Mail-based Solution for Botnet Detection and Network Bandwidth Protection
★ Shark: Phishing Information Recycling from Spam Mails	★ FFRTD: Beat Fast-Flux by Response Time Differences
★ Antivirus Software Shield against Antivirus Terminators	★ MAC-YURI : My ACcount, YoUr ResponsIbility
★ KKBB: Kernel Keylogger Bye-Bye	★ CIDP Treatment: An Innovative Mobile Botnet Covert Channel based on Caller IDs with P8 Treatment

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

真正資訊隱藏技術是將機密資訊從負載影像嵌入後，在將影像傳送給對方後，在由對方利用相同演算法將機密訊息取出。真正可逆資訊隱藏是可以完完全全取出機密訊息並且讓影像恢復原始樣貌。對於一些敏感的影像的應用，例如：醫學影像、軍事影像或是衛星影像等方面是很重要影像。因為這類的敏感影像是不容許出現些微的失真，這種情況造成應用上誤判。本論文將設計具影像可逆式資訊隱藏技術在這類的敏感影像。故而發展出真正可逆式資訊隱藏技術，它目的是將機密取出後可以徹底地還原出原始影像的容貌，可以避免發生任何因藏入後的影像失真並且不讓秘密訊息的任何型態被第三者所偵測到。
在此篇論文中，我們將提出四種資訊隱藏技術植基於直方圖處理機制。在我們提出的方法中不但能夠處理自然影像外還能夠處理非自然影像。在先前處理上我們對影像分析以及溢位處理。在影像分析方面能夠分辨影像是否為自然影像或是非自然影像。在溢位處理能夠在嵌入機密訊息後能夠避免像素溢位。接下來我們將介紹本論文中提出各式各樣的預測方式並且這一些預測方法能夠增加嵌入量以外還可以提昇影像品質。就像是在自然影像中的邊吻合方式預測則是使用被預測像素的四周來做預測或是九宮格預測方式則是利用被預測像素四周像素分成兩群、四群、八群等。接下來，使用一個權重的策略來做預測，利用影像漸層關係，像素的左右兩邊像素值差異為最小之關係；則對相同列像素加一個兩倍或是三倍之權重。使用一個類似動態規劃預測方法，在每次預測中找出影像品質較高的預測方法並將它當成下一次的預測。在預測策略之後將產生一張差值影像並且利用直方圖方法找出差值影像中高點與零點。最後產生一張偽裝影響。我們所提出方法能夠保證在第一次嵌入機密訊息中讓PSNR保持48dB並且在不會影響人類視覺品質PSNR大約30dB並且在多次嵌入會比其他方法會有較高的嵌入量與影像品質。

摘要(英)

The real reversible information concealment technique embeds secret information from the image carrier. After the information is embedded in the image carrier, the image is transferred from the Internet. Secret information is extracted with the same algorithm that is used to recover the stego image, which hides the information image. This concealment technique can completely extract the image and return it to its original appearance. Image quality is very important for sensitive imaging applications, such as medical, military, and satellite imaging. These sensitive images must not appear even slightly distorted; the recovered images can be misinterpreted if any distortion is present. In this dissertation, we propose a reversible information concealment technique for sensitive images. Using the proposed method, real reversible information concealment techniques can avoid image distortion while being undetected by application software.
In this dissertation, we first discuss four types of information concealment based on a histogram mechanism. In our proposed scheme, both natural and non-natural images can be processed. In the pre-processing phase, images are processed by analysis and overflow (or underflow). In the analysis of these images, natural and non-natural images can be distinguished. The overflow/underflow process can prevent overflowing/underflowing of pixels after the secret information is embedded. Furthermore, various prediction strategies can be used to increase the image capacity and quality. For example, a side-match scheme employs the surrounding pixels or a 3 × 3 block to predict the values of the pixels that are divided into two, four, eight, or more classes in the natural images. From that point, a weight strategy uses the predicted pixel values of each of the proximate sides and doubles or triples these pixels for the respective sides. A threshold strategy is then used to prevent image pixel underflow/overflow. A similar dynamic programming approach is then used after each prediction to select the highest image quality for the next predicted image. Then, a difference image is generated to find the peak and zero points using the histogram method. The selected pixels are situated between the peak and zero points by shifting and embedding. Accordingly, a stego image is obtained. The peak signal-to-noise ratio (PSNR) can be maintained at approximately 48 dB, while human perception is not affected under image visual quality of approximately 30 dB. Based on our experimental results, the capacity of the proposed method is higher than those of existing methods.

關鍵字(中)

★ Nature image
★ Non-nature image
★ Reversible
★ Information hiding
★ weighted average
★ Dynamic program process

關鍵字(英)

★ 自然影像
★ 非自然影像
★ 可逆
★ 資訊隱藏
★ 平均權重
★ 動態規劃

論文目次

中文摘要 i
ABSTRACT iii
Index v
List of Figures vi
List of Tables vii
Chapter 1. Introduction 1
Chapter 2. Related Work 7
2.1. Ni et al.’s method 7
2.2. Li et al.’s method 9
2.3. Method of Sachnev et al. 12
2.4. Yang and Tsai’s scheme 14
Chapter 3. Reversible Information hiding in Image Systems by Means of Histogram-shifting Strategy 15
3.1. Proposed Scheme 15
3.2. Pre-processed phase 16
3.3. Embedding algorithm 20
3.4. Extracting and reversing the algorithm 25
3.5. Experiments and Discussions 27
3.6. Summaries 32
Chapter 4. Reversible Information Hiding in Image Systems on the Basis of Similar Dynamic Program Prediction Strategy 33
4.1. Proposed Scheme 34
4.2. Pre-processing of underflow or overflow 36
4.3. Embedding algorithm 38
4.4. Extraction and reversal algorithm 42
4.5. Other prediction algorithms 45
4.6. Experiment results 46
4.7. Summaries 57
Chapter 5. Conclusion and Future Work 58

參考文獻

[1] M. Al Ameen, J. Liu, and K. Kwak, “Security and privacy issues in wireless sensor networks for healthcare applications,” Journal of medical systems, vol. 36, pp. 93-101, 2012.
[2] Z. Ni, Y. Q. Shi, N. Ansari, and W. Su, “Reversible data hiding,” IEEE Transactions on circuits and systems for video technology, vol. 16, pp. 354-362, 2006.
[3] C. Lin and N. Hsueh, “A lossless data hiding scheme based on three-pixel block differences,” Pattern Recognition, vol. 41, pp. 1415-1425, 2008.
[4] P. Tsai, Y. Hu, and H. Yeh, “Reversible image hiding scheme using predictive coding and histogram shifting,” Signal Processing, vol. 89, pp. 1129-1143, 2009.
[5] X. Zeng, L. Ping, and Z. Li, “Lossless data hiding scheme using adjacent pixel difference based on scan path,” Journal of Multimedia, vol. 4, pp. 145-152, 2009.
[6] J. Y. Hsiao, K. F. Chan, and J. Morris Chang, “Block-based reversible data embedding,” Signal Processing, vol. 89, pp. 556-569, 2009.
[7] Y. C. Li, C. M. Yeh, and C. C. Chang, “Data hiding based on the similarity between neighboring pixels with reversibility,” Digital Signal Processing, vol. 20, pp. 1116-1128, 2010.
[8] Z. Zhao, H. Luo, Z. M. Lu, and J. S. Pan, “Reversible data hiding based on multilevel histogram modification and sequential recovery,” AEU-International Journal of Electronics and Communications, vol. 65, pp. 814-826, 2011.
[9] H. Luo, F. X. Yu, H. Chen, Z. L. Huang, H. Li, and P. H. Wang, “Reversible data hiding based on block median preservation,” Information Sciences, vol. 181, pp. 308-328, 2011.
[10] J. Tian, “Reversible data embedding using a difference expansion,” IEEE Trans. Circuits Syst. Video Techn., vol. 13, pp. 890-896, 2003.
[11] A. M. Alattar, “Reversible watermark using the difference expansion of a generalized integer transform,”IEEE Transactions on Image Processin , vol. 13, pp. 1147-1156, 2004.
[12] H. J. Kim, V. Sachnev, Y. Q. Shi, J. Nam, and H. G. Choo, “A novel difference expansion transform for reversible data embedding,” IEEE Transactions on Information Forensics and Security, vol. 3, pp. 456-465, 2008.
[13] V. Sachnev, H. J. Kim, J. Nam, S. Suresh, and Y. Q. Shi, “Reversible watermarking algorithm using sorting and prediction,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, pp. 989-999, 2009.
[14] O. M. Al-Qershi and B. E. Khoo, “High capacity data hiding schemes for medical images based on difference expansion,” Journal of Systems and Software, vol. 84, pp. 105-112, 2011.
[15] M. Liu, H. S. Seah, C. Zhu, W. Lin, and F. Tian, “Reducing location map in prediction-based difference expansion for reversible image data embedding,” Signal Processing, vol. 92, pp. 819-828, 2012.
[16] C. H. Yang and Y. C. Lin, “Reversible data hiding of a VQ index table based on referred counts,” Journal of Visual Communication and Image Representation, vol. 20, pp. 399-407, 2009.
[17] Z. M. Lu, J. X. Wang, and B. B. Liu, “An improved lossless data hiding scheme based on image VQ-index residual value coding,” Journal of Systems and Software, vol. 82, pp. 1016-1024, 2009.
[18] J. Fridrich, M. Goljan, and R. Du, “Invertible authentication,” in Photonics West 2001-Electronic Imaging, 2001, pp. 197-208.
[19] J. Fridrich, M. Goljan, and R. Du, “Lossless data embedding: new paradigm in digital watermarking,” EURASIP Journal on Applied Signal Processing, vol. 2002, pp. 185-196, 2002.
[20] Y. Li, C. Yeh, and C. Chang, “Data hiding based on the similarity between neighboring pixels with reversibility,” Digital Signal Processing, vol. 20, pp. 1116-1128, 2010.
[21] C.T. Wang and H.F. Yu, “A Markov-based reversible data hiding method based on histogram shifting,” Journal of Visual Communication and Image Representation, pp. 798-811, 2012.
[22] C. H. Yang and M. H. Tsai, “Improving histogram-based reversible data hiding by interleaving predictions,” Image Processing, IET, vol. 4, pp. 223-234, 2010.
[23] K. S. Kim, M. J. Lee, H. Y. Lee, and H. K. Lee, “Reversible data hiding exploiting spatial correlation between sub-sampled images,” Pattern Recognition, vol. 42, pp. 3083-3096, 2009.
[24] C. C. Lin and N. L. Hsueh, “A lossless data hiding scheme based on three-pixel block differences,” Pattern Recognition, vol. 41, pp. 1415-1425, 2008.
[25] C. H. Yang and M. H. Tsai, “Improving histogram-based reversible data hiding by interleaving predictions,” IET image processing, vol. 4, pp. 223-234, 2010.
[26] P. Tsai, Y. C. Hu, and H. L. Yeh, “Reversible image hiding scheme using predictive coding and histogram shifting,” Signal Processing, vol. 89, pp. 1129-1143, 2009.
[27] L. Kamstra and H. J. Heijmans, “Reversible data embedding into images using wavelet techniques and sorting,” IEEE Transactions on Image Processing, pp. 2082-2090, 2005.
[28] D. M. Thodi and J. J. Rodríguez, “Expansion embedding techniques for reversible watermarking,” IEEE Transactions on Image Processing, pp. 721-730, 2007.
[29] I. C. Chang, Y. C. Hu, W. L. Chen, and C. C. Lo, “High capacity reversible data hiding scheme based on residual histogram shifting for block truncation coding,” Signal Processing, vol. 108, pp. 376-388, 2015.
[30] Y. Yang, W. Zhang, D. Liang, and N. Yu, “Reversible data hiding in medical images with enhanced contrast in texture area,” Digital Signal Processing, vol. 52, pp. 13-24, 2016.

指導教授

許富皓(Fu-Hau Hsu)

審核日期

2016-7-21

推文