現今網路與影像編輯軟體的發展，使得數位資訊的修改與編輯更為容易。因此影像的可信度成為一個課題。在數位影像偽造中，複製-移動偽造與拼接偽造均為常見的偽造攻擊。複製-移動的篡改是將影像中的一個區塊複製，再將此複製區塊貼在同張影像中的不同位置，企圖覆蓋可能存在的重要資訊；拼接篡改則是複製影像中的一個區塊，將其貼至另一張影像中，試圖將原本不屬於該影像的內容加入影像當中。
為此本論文提出一個卷積神經網路（CNN）的模型，針對遭到複製-移動或是拼接偽造的影像進行偵測。首先，將影像切割成固定且不重疊的區塊並利用雷登轉換提取特徵。接著，利用這些前處理過的資料進行學習網路的訓練，使本篇論文所提出的網路架構模型能分類出偽造與真實的影像區塊。最後，透過上述分類進行偽造與真實的標記，偽造區塊就能被偵測出來。實驗結果顯示，所提出之方法較其他文獻方法具有更好的準確率。

With the Internet development and the availability of image editing tools, digital images can be easily manipulated and edited. Therefore, the credibility of digital images has faced severe challenges. In digital image forensic, the copy-move and splicing forgeries are popular forgery attacks. For copy-move forgery, a part of the image is copied and pasted elsewhere in the same image in order to cover possible important messages.
However, the image splicing is to duplicate a region of another image to the original image so as to add the contents not belonging to the original image.
In this thesis, a convolutional neural network (CNN) model is proposed to detect such tampering. First, the image is divided into fixed-size non-overlapping patches and the Radon transform is applied to each patch to compute the features.
After the network is trained, the proposed model can classify the tampered and the authentic patches. By classifying each patch in the images, the duplicated regions can be detected. The experimental results demonstrate that the accuracy of proposed method is better than other methods.

[1] T. Qazi, K. Hayat, S. U. Khan, S. A. Madani, I. Khan, J.Kołodziej, et al., “Survey on blind image forgery detection,” in Image Processing, IET, vol. 7, pp. 660-670, 2013.
[2] K. Asghar, Z. Habib, and M. Hussain, “Copy-move and splicing image forgery detection and localization techniques: a review,” in Australian Journal of Forensic Sciences, pp. 1-27, 2016.
[3] F. Y. Shih, “Multimedia Security: Watermarking, Steganography, and Forensics,” in CRC Press, 2012.
[4] A. Khan, S. A. Malik, A. Ali, R. Chamlawi, M. Hussain, M. T. Mahmood, et al., “Intelligent reversible watermarking and authentication: Hiding depth map information for 3D cameras,” in Information Sciences, vol. 216, pp. 155-175, 2012.
[5] W.-C. Hu, W.-H. Chen, D.-Y. Huang, and C.-Y. Yang, “Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha mattes,” in Multimedia Tools and Applications, vol. 75, pp. 3495-3516, 2016.
[6] C.-H. Tzeng and W.-H. Tsai, “A new technique for authentication of image/video for multimedia applications,” in Proceedings of the 2001 workshop on Multimedia and security: new challenges, pp. 23-26, 2001.
[7] C.-S. Lu and H.-Y. Liao, “Structural digital signature for image authentication: an incidental distortion resistant scheme,” in Multimedia, IEEE Transactions on, vol. 5, pp. 161-173, 2003.
[8] J. Fridrich, D. Soukal, and J. Lukas, “Detection of Copy-Move Forgery in Digital Images,” in Proceedings of Digital Forensic Research Workshop, Aug. 2003.
[9] Y. Huang, W. Lu, W. Sun, and D. Long, “Improved DCT-based detection of copy-move forgery in images,” in Forensic science international, vol. 206, no. 1, pp. 178-184, 2011.
[10] A. C. Popescu and H. Farid, “Exposing Digital Forgeries by Detecting Traces of Resampling,” in IEEE Transactions on Signal Processing, Vol. 53, pp. 758-767, 2005.
[11] H. J. Lin, C. W. Wang, and Y. T. Kao, “Fast Copy-Move Forgery Detection,” in WSEAS Transactions on Signal Processing., vol. 5, no. 5, pp. 188–197, 2009.
[12] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” in International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.
[13] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” in Computer vision–ECCV 2006, pp. 404-417: Springer, 2006.
[14] Atefeh Shahroudnejad and Mohammad Rahmati, “Copy-Move Forgery Detection in Digital Images Using Affine-SIFT,” in 2016 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS), 1-5, 2016.
[15] C. Guillemot and O. Le Meur, “Image inpainting: Overview and recent advances,” in IEEE signal processing magazine, 31(1):127–144, 2014.
[16] G. Muhammad, M. H. Al-Hammadi, M. Hussain, and G. Bebis, “Image forgery detection using steerable pyramid transform and local binary pattern,” in Machine Vision and Applications, 25(4):985–995, 2014.
[17] J. Dong and W. Wang, “CASIA Tampered Image Detection Evaluation Database,” 2011. http://forensics.idealtest.org/casiav2/
[18] B. Bayar and M. C. Stamm, “A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer,” in Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 5–10, 2016.
[19] B. Bayar and M. C. Stamm, “On the robustness of constrained convolutional neural networks to jpeg post-compression for image resampling detection,” in Proceedings of The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing, 2017.
[20] J. Chen, X. Kang, Y. Liu, and Z. J. Wang, “Median Filtering Forensics Based on Convolutional Neural Networks,” in IEEE Signal Processing Letters, vol. 22, no. 11, pp. 1849-1853, Nov. 2015.
[21] Y. Liu, Q. Guan, X. Zhao, and Y. Cao, “Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks,” in Conference on Computer Vision and Pattern Recognition (CVPR), Feb. 7, 2018.
[22] P. Rota, E. Sangineto, V. Conotter, and C. Pramerdorfer, “Bad teacher or unruly student: Can deep learning say something in image forensics analysis?,” in Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), 2016.
[23] Y. Zhang, J. Goh, L. Win, and V. Thing, “Image Region Forgery Detection: A Deep Learning Approach,” in Proceedings of the Singapore Cyber-Security Conference (SG-CRC), vol. 14, p. 1–11, 2016.
[24] B. Mahdian and S. Saic, “Blind authentication using periodic properties of interpolation,” in Information Forensics and Security, IEEE Transactions, Sep. 2008.
[25] K. Fukushima and S. Miyake, “Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position,” in Pattern recognition, vol. 15, no. 6, pp. 455-469, 1982.
[26] Y. LeCun et al., “Handwritten digit recognition with a back-propagation network,” in Advances in neural information processing systems, pp. 396-404, 1990.
[27] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097-1105, 2012.
[28] A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. ICML, vol. 30, no. 1, 2013.
[29] N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” in Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
[30] K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” in Biol. Cybern., vol. 36, no. 4, pp. 193–202, Jan. 1980.
[31] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” in Neural computation, 1989.
[32] E. Gopi, N. Lakshmanan, T. Gokul, S. KumaraGanesh, and P. R. Shah, “Digital image forgery detection using artificial neural network and auto regressive coefficients,” in Electrical and Computer Engineering (CCECE), pp. 194-197, 2006.
[33] J. Luka’s and J. Fridrich, “Estimation of primary quantization matrix in double compressed jpeg images,” in Proc. Digital Forensic Research, Workshop, pp. 5-8, 2003.
[34] M.K. Johnson and H. Farid, “Exposing digital forgeries by detecting inconsistencies in lighting,” in ACM Multimedia and Security Workshop, 2005.
[35] “TensorFlow.” https://www.tensorflow.org/
[36] R. C. Gonzalez and R. E. Woods, “Digital Image Processing, 3rd Edition,” in Pearson, Aug. 2008.
[37] W. Wang, J. Dong, and T. Tan, “Effective image splicing detection based on image chroma,” in IEEE International Conference on Image Processing, pp. 1257–1260, Nov. 2009.
[38] Sheng-An Tsai, “Image Forgery Detection and Localization Based on Convolutional Neural Network,” in Master’s thesis, National Cheng Kung University, 2018.
[39] Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, “Detection and Localization of Image Forgeries using Resampling Features and Deep Learning,” in Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 3 2017.
[40] Z. He, W. Lu, W. Sun and J. Huang, “Digital image splicing detection based on Markov features in DCT and DWT domain,” in Pattern Recognition, vol. 45, no. 12, pp. 4292-4299, Dec. 2012.
[41] Ghulam Muhammad, Munner H. Al-Hammadi, Muhammad Hussain, George Bebis, “Image forgery detection using steerable pyramid transform and local binary pattern,” in Machine Vision and Applications, pp. 1-11, 2013.