Copy-move Forgeries Detection Based on Polar Sine Transform
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School of Electronic & Information Engineering, Hebei University of Technology, Tianjin 300401, China
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摘要:
該文使用極坐標正弦變換(PST)特征對圖像進行Copy-move篡改檢測,將待檢測圖像轉(zhuǎn)換成灰度圖并進行PST特征提取,并采用改進的快速近似最近鄰搜索算法PatchMatch對特征描述符進行匹配,以克服匹配全局描述符帶來的處理時間較長的缺點。實驗分析表明,該文所提方法不僅對圖像的線性Copy-move篡改和旋轉(zhuǎn)干擾篡改有很好的效果,而且對噪聲和JPEG壓縮干擾篡改也具有一定的魯棒性。最后對綜合干擾篡改實驗測試發(fā)現(xiàn),在綜合篡改幅度較小的情況下,準確率可以達到98.0%。
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關(guān)鍵詞:
- 圖像檢測 /
- 圖像篡改 /
- 極坐標正弦變換 /
- PatchMatch
Abstract:Polar Sine Transform (PST) is used to detect Copy-move forgeries in the paper, and the image to be detected is transformed into gray scale image and feature extraction is carried out by PST. Improved PatchMatch, a fast approximate nearest neighbor search algorithm, is used to match feature descriptors to overcome the problem of long time consuming caused by matching global descriptors. Experiments show that the proposed method is not only effective for linear Copy-move forgeries and rotation interference forgeries, but also robust to noise and JPEG compression interference forgeries. Finally, the experimental results of synthetic interference forgeries show that the accuracy can reach 98.0% when the synthetic forgeries range is small.
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Key words:
- Image detection /
- Image forgery /
- Polar Sine Transform (PST) /
- PatchMatch
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AL-QERSHI O M and KHOO B E. Passive detection of copy-move forgery in digital images: State-of-the-art[J]. Forensic Science International, 2013, 231(1/3): 284–295. doi: 10.1016/j.forsciint.2013.05.027 ZHOU Xinmin, WANG Kaiyuan, and FU Jian. A method of SIFT simplifying and matching algorithm improvement[C]. IEEE 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), Wuhan, China, 2016: 73–77. doi: 10.1109/ICIICII.2016.0029. AHSAN A M and MOHAMAD D B. Machine learning technique for object detection based on SURF feature[J]. International Journal of Computational Vision and Robotics, 2017, 7(1/2): 6–19. doi: 10.1504/IJCVR.2017.081232 FARID H. Image forgery detection[J]. IEEE Signal Processing Magazine, 2009, 26(2): 16–25. doi: 10.1109/MSP.2008.931079 PIVA A. An overview on image forensics[J]. ISRN Signal Processing, 2013, 2013: 496701. AL-QERSHI O M and KHOO B E. Enhanced matching method for copy-move forgery detection by means of Zernike moments[C]. The 13th International Workshop on Digital-Forensics and Watermarking, Taipei, China, 2014: 485–497. doi: 10.1007/978-3-319-19321-2_37. 閆旭, 姜威, 賁晛燁. 基于改進Hu不變矩的圖像篡改檢測算法[J]. 光學(xué)技術(shù), 2018, 44(2): 171–176. doi: 10.13741/j.cnki.11-1879/o4.2018.02.008YAN Xu, JIANG Wei, and BEN Xianye. Image tamper detection algorithm based on improved Hu invariant moments[J]. Optical Technique, 2018, 44(2): 171–176. doi: 10.13741/j.cnki.11-1879/o4.2018.02.008 AMERINI I, BALLAN L, CALDELLI R, et al. A SIFT-based forensic method for copy–move attack detection and transformation recovery[J]. IEEE Transactions on Information Forensics and Security, 2011, 6(3): 1099–1110. doi: 10.1109/TIFS.2011.2129512 MUHAMMAD G, HUSSAIN M, KHAWAJI K, et al. Blind copy move image forgery detection using dyadic undecimated wavelet transform[C]. The 17th IEEE International Conference on Digital Signal Processing, Corfu, Greece, 2011. doi: 10.1109/ICDSP.2011.6004974. CHRISTLEIN V, RIESS C, JORDAN J, et al. An evaluation of popular copy-move forgery detection approaches[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(6): 1841–1854. doi: 10.1109/TIFS.2012.2218597 YAP P T, JIANG Xudong, and KOT A C. Two-dimensional polar harmonic transforms for invariant image representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(7): 1259–1270. doi: 10.1109/TPAMI.2009.119 李揚, 吳敏淵, 顏佳. 基于改進PatchMatch的自相似性圖像超分辨率算法[J]. 計算機應(yīng)用研究, 2018, 35(4): 1231–1235. doi: 10.3969/j.issn.1001-3695.2018.04.058LI Yang, WU Minyuan, and YAN Jia. Self-similarity based image super-resolution algorithm using optimized PatchMatch[J]. Application Research of Computers, 2018, 35(4): 1231–1235. doi: 10.3969/j.issn.1001-3695.2018.04.058 BARNES C, SHECHTMAN E, FINKELSTEIN A, et al. PatchMatch: A randomized correspondence algorithm for structural image editing[J]. ACM Transactions on Graphics, 2009, 28(3): No. 24. doi: 10.1145/1531326.1531330 BARNES C, SHECHTMAN E, GOLDMAN D B, et al. The generalized PatchMatch correspondence algorithm[C]. The 11th European Conference on Computer Vision–ECCV 2010, Heraklion, Greece, 2010: 29–43. doi: 10.1007/978-3-642-15558-1_3. COZZOLINO D, POGGI G, and VERDOLIVA L. Efficient dense-field Copy-move forgery detection[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(11): 2284–2297. doi: 10.1109/TIFS.2015.2455334 EHRET T and ARIAS P. On the convergence of PatchMatch and its variants[C]. 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018: 1121–1129. doi: 10.1109/CVPR.2018.00123. EHRET T. Automatic detection of internal copy-move forgeries in images[J]. Image Processing on Line, 2018(8): 167–191. doi: 10.5201/ipol.2018.213 -