近幾年來，科技快速地發展使得行動裝置普及，拍攝數位照片變得越來越容易，再加上數位影像的技術愈來愈成熟，影像編輯軟體也因此盛行。由於數位影像的內容資訊容易被修改，衍生數位影像鑑識的議題。因此該如何確認其攝影內容的真實性變得越來越重要。本研究的目的在於快速偵測經過鈍化遮罩處理後的影像，也就是將銳化後的影像偵測出來，以垂直邊緣二進位編碼演算法為基礎加以改良，利用格雷碼對稱的特性，降低特徵的運算量，改善其執行時間。再加上使用Otsu門檻化方法搭配Canny邊緣偵測，保留對比明顯的邊緣，增加辨識的成功率。最後，比較兩種編碼方式並且觀察原始影像與銳化後影像的特徵對分類結果的影響。
經由實驗結果顯示，本論文之改良式鈍化遮罩偵測系統，對於一般拍攝環境下，經過鈍化遮罩處理過的影像具有快速且較佳的檢測效果。

In recent years, the development of the wireless technologies is growing rapidly. People generally have more than one mobile device such as smart phones or tablet PCs. Therefore, taking a picture becomes a simple thing in our live. Due to the fact that digital image processing software is easy to use, the research of digital image forensics becomes popular in the world.
In this study, we focus on Unsharp Masking (USM) detection. The proposed detecting system is based on Edge Perpendicular Binary Coding (EPBC). We use Otsu thresholding to enhance the performance of Canny edge detection, so that the accuracy of USM detection is increased. Moreover, the symmetric property of Gray encoding is used to reduce the number of feature points. This improves the execution time of the detecting system.
Experimental results show that our proposed method has faster execution and better accuracy of USM detection for the normal shooting environment.

[1] 資策會FIND2014(H2)「2014臺灣消費者行動裝置暨APP使用行為研究調查報告」http://www.find.org.tw/market_info.aspx?n_ID=8303
[2] H. Farid, “Digital image forensics,” Sci. Amer., vol. 298, no. 6, pp.66-71, 2008.
[3] A. Piva, “An overview on image forensics,” in ISRN Signal Process., vol. 2013, 2013.
[4] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ, USA: Prentice-Hall, 2007.
[5] H. Farid, “Image forgery detection,” in IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 16-25, 2009.
[6] G. Cao, Y. Zhao, and R. Ni, “Detection of image sharpening based on histogram aberration and ringing artifacts,” in Proc. IEEE Int. Conf. Multimedia and Expo (ICME), pp. 1026-1029, 2009.
[7] G. Cao, Y. Zhao, R. Ni, and A. C. Kot, “Unsharp masking sharpening detection via overshoot artifacts analysis,” in IEEE Signal Process. Lett., vol. 18, no. 10, pp. 603-606, 2011.
[8] F. Ding, G. Zhu, and Y. Q. Shi, “A novel method for detecting image sharpening based on local binary pattern,” in Int. Workshop on Digitalforensics and Watermarking (IWDW), vol. 8389, pp. 180-191, 2014.
[9] F. Ding, G. Zhu, J. Yang, J. Xie and Y. Q. Shi, “Edge Perpendicular Binary Coding for USM Sharpening Detection” in IEEE Signal Process. Lett., vol. 22, no. 3, pp. 327-331, 2015.
[10] Otsu N., “A Threshold Selection Method from Gray-Level Histograms,” in IEEE Trans. System, Man and Cybernetics, vol. 9, no. 1, pp.62-66, 1979.
[11] M. Fang, G. X. Yue, and Q. C. Yu, “The Study on An Application of Otsu Method in Canny Operator”, in Proceedings of the 2009 International Symposium on Information Processing, Huangshan, P. R. China, pp. 109-112, August 2009..
[12] J. Gao and N. Liu, “An improved adaptive threshold canny edge detection algorithm”, in International Conference on Computer Science and Electronics Engineering, pp. 164-168, 2012.
[13] G. Hao, L. Min, and H. Feng, “Improved self-adaptive edge detection method based on Canny,” in International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 527-530, 2013.
[14] Y. K. Huo, G. Wei, Y. D. Zhang and L. N. Wu, “An adaptive threshold for the Canny Operator of edge detection,” in International Conference on Image Analysis and Signal Processing, pp. 371-374, 2010.
[15] H. Peng, R. Zhai, S. Liu, Y. Wen, and L. Wu, “Edge Detection of Growing Citrus Based on Self-adaptive Canny Operator,” International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM), pp. 342-345, 2011.
[16] J. F. Canny. “A computational approach to edge detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 8, no. 6, pp. 679-698, 1986.
[17] 林宗勳，Support Vector Machines 簡介，參閱至：http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/tutorials/SVM2.pdf
[18] LIBSVM入門網站，參閱至： http://www.csie.ntu.edu.tw/~piaip/docs/svm/#
[19] LIBSVM相關資訊網站，參閱至： http://www.csie.ntu.edu.tw/~cjlin/libsvm/
[20] Microsoft office趨勢線介紹，參閱至：
https://support.office.com/zh-tw/article/%E6%96%B0%E5%A2%9E%E3%80%81%E8%AE%8A%E6%9B%B4%E6%88%96%E7%A7%BB%E9%99%A4%E5%9C%96%E8%A1%A8%E4%B8%AD%E7%9A%84%E8%B6%A8%E5%8B%A2%E7%B7%9A-fa59f86c-5852-4b68-a6d4-901a745842ad
[21] 回歸分析，參閱至：http://www.cc.ntut.edu.tw/~jcjeng/Ch10_regression.pdf
[22] Y. L. Lee, H . C. Kim, and H. W. Park,“Blocking Effect Reduction of JPEG Images by Signal Adaptive Filter,” IEEE Trans. on Image Processing, vol. 7, no. 2, pp. 229-234, 1998
[23] 陳文儉，郭子榮，「利用線性濾波技術於降低區塊效應之研究」，Journal of Information Technology and Applications，vol. 3, no. 1, pp. 55-65, 2008
[24] NRCS影像圖庫參閱至： http://photogallery.nrcs.usda.gov/res/sites/PhotoGallery/index.html
[25] UCID影像圖庫參閱至：http://homepages.lboro.ac.uk/~cogs/datasets/ucid/ucid.html
[26] 接收者操作特徵曲線(ROC)參閱至維基百科：https://zh.wikipedia.org/wiki/ROC%E6%9B%B2%E7%BA%BF