Title

利用多尺度區域雜訊不一致性之影像拼接偵測

Translated Titles

Image Splicing Detection Using Inconsistent Multi-scale Local Noise Variances

Authors

卓宥亦

Key Words

影像鑑識 ; 篡改偵測 ; 吉伯斯隨機場 ; 主成分分析 ; 多尺度 ; Image forensics ; Tampering detection ; Gibbs random field ; Principal component analysis ; Multi-scale

PublicationName

臺北科技大學電機工程系所學位論文

Volume or Term/Year and Month of Publication

2013年

Academic Degree Category

碩士

Advisor

郭天穎

Content Language

繁體中文

Chinese Abstract

由於影像編輯軟體快速的發展,使得數位影像可藉由影像編輯軟體修改其影像內容資訊,成為一張全新的影像。外來拼接篡改為一種簡單又常見的篡改方式。此種竄改操作通常是複製影像的一個區域,貼至另一張影像當中,試圖將原本不屬於該影像的內容加入到影像當中,因此數位影像的真實性備受考驗。 一張影像的生成,通常會經由不同的感測器與後處理而產生,所以不同來源的影像會含有不同大小的雜訊量,基於此現象,本論文利用多尺度主成分分析估測雜訊程度演算法來偵測影像拼接,以改善現有文獻偵測率之問題。此外,我們利用EGB 影像分割(segmentation)的結果,與先做雜訊程度估測再經分群後的結果做結合,用以獲得最佳的篡改區域定位圖。 在實驗階段,我們用哥倫比亞圖像拼接檢驗資料庫(Columbia uncompressed image splicing detection evaluation dataset)當作我們的測試資料庫,從實驗結果顯示,我們提出利用多尺度區域雜訊不一致性之影像拼接偵測較現有文獻方法更加強健與準確。

English Abstract

Due to the rapid development of image editing software, it becomes easy to tamper a digital image and create a fake image. One of the common and simple ways of the digital image tampering is image splicing, which is to copy a region of an image to another image, and add the contents not belonging to the original image. Therefore, the authenticity of digital image has become an important issue. Since different sources of images are productions of different sensors and post-processing, they will contain different noise levels. Based on this fact, we propose a multi-scale PCA noise level estimation algorithm to detect image splicing, and improve the detection rate of the existing works. In addition, our method can locate the tampering region by clustering the estimated noise with the integration of EGB segmentation. In the experiments, we use the Columbia uncompressed image splicing detection evaluation dataset to test our method. The experimental results show that our method can detect the image splicing robustly and accurately.

Topic Category 電資學院 > 電機工程系所
工程學 > 電機工程
Reference
  1. [1] A. Haouzia and R. Noumeir, “Methods for Image Authentication: A Survey,” Springer Netherlands, Multimedia Tools and Applications, Vol. 39. No. 1, pp. 1-46, Aug. 2008.
    連結:
  2. [2] H. Farid, “Digital Image Forensics,” Scientific American Magazine, pp. 66-71, Jun. 2008.
    連結:
  3. [3] H. T. Sencar and N. Memon, “Overview of State-of-the-Art in Digital Image Forensics,” Statistical Science and Interdisciplinary Research. World Scientific Press, Singapore, 2008.
    連結:
  4. [4] B. Mahdian and S. Saic, “Blind Methods for Detecting Image Fakery,” IEEE International Carnahan Conference on Security Technology, pp. 280-286, Oct. 2008.
    連結:
  5. [5] T. V. Lanh, K. S. Chong, S. Emmanuel and M. S. Kankanhalli, “A Survey on Digital Camera Image Forensic Methods,” IEEE International Conference on Multimedia and Expo, pp. 16-19, Jul. 2007.
    連結:
  6. [6] W. Luo, Z. H. Qu, F. Pan and J. Huang, “A Survey of Passive Technology for Digital Image Forensics,” Frontiers of Computer Science in China, Vol. 1, pp. 166-179, May 2007.
    連結:
  7. [7] H. Cao and A. C. Kot, “Accurate Detection of Demosaicing Regularity for Digital Image Forensics,” IEEE Transactions on Information Forensics and Security, Vol. 4, No. 4, pp. 899-910, Dec. 2009.
    連結:
  8. [9] T. T. Ng, S. F. Chang, J. Hsu and M. Pepeljugoski, “Columbia Photographic Images and Photorealistic Computer Graphics Dataset,” Columbia University, ADVENT Technical Report #205-2004-5, Feb. 2005.
    連結:
  9. [10] A. C. Popescu and H. Farid, “Exposing Digital Forgeries by Detecting Traces of Resampling,” IEEE Transactions on Signal Processing, Vol. 53, No. 2, pp. 758-767, Feb. 2005.
    連結:
  10. [11] B. Mahdian and S. Saic, “Using noise inconsistencies for blind image forensics,” Journal: Image and Vision Computing - IVC , vol. 27, no. 10, pp. 1497-1503, Sep. 2009.
    連結:
  11. [14] A. E. Dirik and N. Memon, “Image Tamper Detection based on Demosaicing Artifacts,” IEEE International Conference on Image Processing, pp. 1497-1500, Nov. 2009.
    連結:
  12. [15] A. C. Popescu and H. Farid, “Exposing Digital Forgeries in Color Filter Array Interpolated Images,” IEEE Transactions on Signal Processing, Vol. 53, No. 10, pp. 3948-3959, Oct. 2005.
    連結:
  13. [16] J. Fridrich, D. Soukal and J. Lukáš; “Detection of Copy-Move Forgery in Digital Images,” Proceedings of Digital Forensic Research Workshop, Cleveland, OH, Aug. 2003.
    連結:
  14. [19] G. H. Li, Q. Wu, D. Tu and S. J. Sun, “A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD,” IEEE International Conference on Multimedia and Expo, pp. 1750-1753, July 2007.
    連結:
  15. [21] Z. C. Lin, J. F. He, X. Tang and C. K. Tang; “Fast, Automatic and Fine-Grained Tampered JPEG Image Detection via DCT Coefficient Analysis,” Pattern Recognition, Vol. 42, Issue 11, pp. 2492-2501, Nov. 2009.
    連結:
  16. [23] S. Pyatykh, J. Hesser and L. Zheng, “Image Noise Level Estimation by Principal Component Analysis,” IEEE Transactions on Image Processing, Vol. 22, pp. 687-699, Feb. 2013.
    連結:
  17. [25] Stan Z. Li, “Markov Random Field Modeling in Image Analysis (2nd Edition),” New York, Springer-Verlag, 2001.
    連結:
  18. [28] J, Park, L. Kurz, “Image enhancement using the modified ICM method,” Image Processing, vol. 5, Issue 5, May 1996, pp. 765-771.
    連結:
  19. [29] P. Felzenszwalb and D. Huttenlocher, “Efficient Graph-Based Image Segmentation,” Int’l J. Comput. Vision, vol. 59, no. 2, pp. 167-181, 2004.
    連結:
  20. [30] 郭天穎、吳家宏,基於馬可夫隨機場之表格文件擷取系統,國立台北科技大學電機工程系碩士論文,中華民國九十八年七月
    連結:
  21. [31] 郭天穎、廖正豪,利用色彩濾片陣列及旋轉不變特性之影像鑑識系統,國立台北科技大學電機工程系碩士論文,中華民國九十九年七月
    連結:
  22. [32] 郭天穎、林家慶,基於相機投影模型與灰暗通道模型之單視角影像2D轉3D技術,國立台北科技大學電機工程系碩士論文,中華民國一百年七月
    連結:
  23. [34] Y. F. Hsu and S. F. Chang, “Columbia Uncompressed Image Splicing Detection Evaluation Dataset”
    連結:
  24. [8] H. Farid, “Creating and Detecting Doctored and Virtual Images: Implications to the Child Pornography Prevention Act,” Department of Computer Science, Dartmouth College, Technical Report, TR2004-518, pp. 1-13, 2004.
  25. [12] X. Pan, X. Zhang and S. Lyu, “Exposing Image Forgery with Blind Noise Estimation,” The 13th ACM Workshop on Multimedia and Security (MM&Sec), Buffalo, NY, September 2011.
  26. [13] X. Pan, X. Zhang and S. Lyu, “Exposing Image Splicing with Inconsistent Local Noise Variances,” Computational Photography (ICCP), 2012 IEEE International Conference on,pp. 1 - 10, 28-29 April 2012.
  27. [17] A. C. Popescu and H. Farid, “Exposing Digital Forgeries by Detecting Duplicated Image Regions,” Department of Computer Science, Dartmouth College, Technical Report TR2004-515, pp. 1-11, 2004.
  28. [18] W. Luo, J. Huang and G. Qiu, “Robust Detection of Region-Duplication Forgery in Digital Image,” International Conference on Pattern Recognition, Vol. 4, pp. 746-749, 2006.
  29. [20] D. Zoran and Y. Weiss. “Scale invariance and noise in nature image,” IEEE International Conference on Computer Vision, Kyoto, Japan, 2009.
  30. [22] H. Farid, “Exposing Digital Forgeries from JPEG Ghosts,” IEEE Transactions on Information Forensics and Security, Vol. 4, No. 1, pp. 154-160, Mar. 2009.
  31. [24] 江祖恕,「馬可夫鏈的簡介」,數學傳播季刊,第九卷,第三期,1985,第43-49頁。
  32. [26] R. Paget, and D. Longstaff, “Extracting the cliques from a neighbourhood system,” Vision, Image and Signal Processing, vol. 144, Issue 3, June 1997, pp. 168-170.
  33. [27] L. Tardon, I. Barbancho, and F. Marquez, “A Markov Random Field Approach to Edge Detection,” IEEE Mediterranean Electrotechnical Conference, 16-19, May 2006, pp. 482-485.
  34. [33] 陳鐘誠的網站, K-Means 分群演算法,
  35. [online] http://ccckmit.wikidot.com/ai:kmeans (Accessed: 25 July 2013).
  36. Available: http://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/