臺灣博碩士論文加值系統

現在因為科技的發達，大量數位攝影機遍佈在公共場所以利調查犯罪行為。但另一方面，大量的攝影機對一般人會有隱私被侵犯的疑慮。在本論文中，我們提出了一個可保護隱私的視訊監視系統，並且可以依照使用者的權力給予適當的保護程度。
首先，我們分割出影片中具有隱私的區域(Privacy-sensitive Region)，針對此區域我們使用數個虛擬隨機排列(Pseudo-randomly Permutation)函式使之越來越模糊。在使用者端我們使用一個身份驗證系統來確認使用者的身份，使用者輸入密鑰本系統即可針對此密鑰還原對應的隱私保護步驟，權力越高者能夠看到越清楚的影片。實驗結果顯示，使用者權力由低到高所看到的隱私保護程度是逐漸的由雜亂到非常清楚。並且此方法可以應用在現有的視訊壓縮標準上且對壓縮效率不會有太大影響。

In recent years, video surveillance system has been widely equipped for crime investigation and deterrence in an attempt to reduce the occurrence of crime events. But the widespread cameras may invade the privacy of innocent persons. In this thesis, we provide a scalable and recoverable video privacy protection system.
First, privacy-sensitive regions are segmented. Then, three permutation functions are employed for scalable and recoverable video privacy protection. Users with different authorization levels will get different authentication keys. Thus, users with the lowest authorization level can only watch the video with the privacy-sensitive region scrambled dramatically and users with the highest authorization level can investigate the original video. Furthermore, the proposed video privacy protection system can be embedded in current video standard codecs with the compression rate being reduced slightly.

CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 A Model for Video Privacy 1
1.3 Related Work 2
1.4 Organization of the Thesis 5
CHAPTER 2 PROPOSED SCALABLE AND RECOVERABLE VIDEO PRIVACY PROTECTION METHOD 6
2.1 Privacy-Sensitive Region Segmentation 7
2.1.1 Segmentation by Background Subtraction 8
2.1.2 Manual Setting of ROI 9
2.2 Scalable Privacy Protection 10
2.2.1 Local AC Permutation 11
2.2.2 Global AC Permutation 13
2.2.3 DC Permutation 14
2.3 Authentication System 16
2.3.1 Authentication Seeds Encryption and Decryption 17
2.4 Drift Prevention 18
2.5 Segmentation Mask Embedding 20
CHAPTER 3 EXPERIMENTAL RESULTS 21
3.1 Scalable Protected Video 21
3.2 Compression Rate 24
3.3 Security Analysis 25
3.3.1 Security of Local AC Permutation 26
3.3.2 Security of Global AC Permutation 28
3.3.3 Security of DC Permutation 29
CHAPTER 4 CONCLUSION 30
REFERENCES 31

[1] A. W. Senior, S. Pankanti, A. Hampapur, L. Brown, Y.-L. Tian, and A. Ekin, “Blinkering Surveillance: Enabling Video Privacy Through Computer Vision,” IBM Tech. Rep. RC22886, NY, USA, 2003.
[2] I. P. Martinez, X. Desurmont, J. Meessen, and J.-F. Delaigle, “Robust Human Face Hiding Ensuring Privacy,” in Proc. Int. Workshop Image Anal. Multimedia Interactive Services (WIAMIS), pp. 5 - 8, Montreux, Switzerland, Apr. 2005.
[3] W. Zhang, S. S. Cheung, and M. Chen, “Hiding Privacy Information in Video Surveillance System,” in Proc. IEEE Int. Conf. Image Process, pp. II-868–II-871 Genova, Italy, Sep. 2005.
[4] Frederic Dufaux and Touradj Ebrahimi, “Scrambling for Privacy Protection in Video Surveillance Systems,” IEEE Transaction on Circuits and Systems for Video Technology, Vol. 18, NO. 8, pp. 1168–1174, Aug. 2008.
[5] G. Li, Y. Ito, X. Yu, N. Nitta, and N. Babaguchi, ” Recoverable Privacy Protection for Video Content Distribution,” EURASIP Journal on Information Security, Vol. 2009, pp.16 - 26, 2009.
[6] S.H. Hung and W.H. Tsai, ” A Study on Authentication and Protection of H.264 Video Contents by Information Hiding Techniques,” Master Thesis, Institute of Computer Science and Engineering, National Chiao Tung University, Taiwan, Jun. 2009.
[7] W. E. L. Grimson, C. Stauffer, R. Romano, and L. Lee, “Using Adaptive Tracking to Classify and Monitor Activities in a Site,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 22 - 29, Santa Barbara, Calif, USA, Jun. 1998.
[8] C. Stauffer and W. E. L. Grimson, “Learning Patterns of Activity Using Real-time Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747 - 757, Aug. 2000.
[9] Image Processing Lab, “TMN (H.263+) encoder/decoder, version 3.0”, University of British Columbia, Sep. 1997.