基于极坐标正弦变换的Copy-move篡改检测

马杰; 钟斌斌; 焦亚男

doi:10.11999/JEIT190481

基于极坐标正弦变换的Copy-move篡改检测

doi: 10.11999/JEIT190481

河北工业大学电子信息工程学院天津 300401

详细信息

作者简介:
马杰：男，1978年生，教授，研究方向为图像处理与模式识别

钟斌斌：男，1990年生，硕士生，研究方向为数字图像处理

焦亚男：女，1992年生，硕士生，研究方向为数字图像处理

通讯作者:
马杰　jma@hebut.edu.cn

中图分类号: TN911.73; TP391.41
计量
- 文章访问数: 2443
- HTML全文浏览量: 1189
- PDF下载量: 58
- 被引次数: 0
出版历程
- 收稿日期: 2019-06-28
- 修回日期: 2019-11-05
- 网络出版日期: 2019-11-28
- 刊出日期: 2020-06-04

Copy-move Forgeries Detection Based on Polar Sine Transform

School of Electronic & Information Engineering, Hebei University of Technology, Tianjin 300401, China

摘要

摘要:
该文使用极坐标正弦变换(PST)特征对图像进行Copy-move篡改检测，将待检测图像转换成灰度图并进行PST特征提取，并采用改进的快速近似最近邻搜索算法PatchMatch对特征描述符进行匹配，以克服匹配全局描述符带来的处理时间较长的缺点。实验分析表明，该文所提方法不仅对图像的线性Copy-move篡改和旋转干扰篡改有很好的效果，而且对噪声和JPEG压缩干扰篡改也具有一定的鲁棒性。最后对综合干扰篡改实验测试发现，在综合篡改幅度较小的情况下，准确率可以达到98.0%。
- 图像检测 /
- 图像篡改 /
- 极坐标正弦变换 /
- 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.
- Image detection /
- Image forgery /
- Polar Sine Transform (PST) /
- PatchMatch

HTML全文

图 1 图像篡改与偏移映射

下载: 全尺寸图片幻灯片

图 2 PatchMatch算法

下载: 全尺寸图片幻灯片

图 3 改进后的传播

下载: 全尺寸图片幻灯片

图 4 PatchMatch算法匹配

下载: 全尺寸图片幻灯片

图 5 后期处理

下载: 全尺寸图片幻灯片

图 6 篡改检测流程图

下载: 全尺寸图片幻灯片

图 7 篡改检测

下载: 全尺寸图片幻灯片

图 8 旋转检测

下载: 全尺寸图片幻灯片

图 9 添加噪声检测

下载: 全尺寸图片幻灯片

图 10 JPEG压缩检测

下载: 全尺寸图片幻灯片

图 11 综合篡改检测

下载: 全尺寸图片幻灯片

图 12 3种算法在不同干扰下的准确率

下载: 全尺寸图片幻灯片

参考文献(17)

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.

闫旭, 姜威, 贲晛烨. 基于改进Hu不变矩的图像篡改检测算法[J]. 光学技术, 2018, 44(2): 171–176. doi: 10.13741/j.cnki.11-1879/o4.2018.02.008

YAN 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

李扬, 吴敏渊, 颜佳. 基于改进PatchMatch的自相似性图像超分辨率算法[J]. 计算机应用研究, 2018, 35(4): 1231–1235. doi: 10.3969/j.issn.1001-3695.2018.04.058

LI 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

施引文献

资源附件(0)

访问统计