基于改进DeepLabv3+的拼接篡改定位检测技术

doi:10.13190/j.jbupt.2018-244

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (1): 68-73.doi: 10.13190/j.jbupt.2018-244

基于改进DeepLabv3+的拼接篡改定位检测技术

张继威, 牛少彰, 曹志义, 王心怡

北京邮电大学智能通信软件与多媒体北京市重点实验室, 北京 100876

收稿日期:2018-09-24 出版日期:2019-02-28 发布日期:2019-03-08
通讯作者: 牛少彰(1963-),男,教授,博士生导师,E-mail:szniu@bupt.edu.cn. E-mail:szniu@bupt.edu.cn
作者简介:张继威(1989-),男,博士生.
基金资助:
国家自然科学基金项目（U1536121，61370195）

Locating Image Splicing by Improved DeepLabv3+

ZHANG Ji-wei, NIU Shao-zhang, CAO Zhi-yi, WANG Xin-yi

Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-09-24 Online:2019-02-28 Published:2019-03-08

摘要/Abstract

摘要： 目前大部分的拼接检测算法主要检测图片是否经历了拼接篡改，而不是对拼接区域进行定位检测，对此，提出了一种基于改进DeepLabv3+的拼接区域定位检测技术.首先，改变原DeepLabv3+网络的分类数；其次，通过改造图像训练库，在训练库中加入含有人物的原图，对原图和篡改图的标签进行区别设置，引导改进的DeepLabv3+网络去学习原图人物和篡改人物特征的区别.实验结果显示，基于改进DeepLabv3+的拼接区域定位检测技术在CASIA数据库上取得了更好的拼接区域定位效果.

关键词: 改进DeepLabv3+, 图像拼接, 区域定位, 轮廓学习, 边缘特征

Abstract: As the existing splicing detection algorithms mainly detect whether the image has undergone image splicing, rather than regional localization of the spliced area. An improved DeepLabv3+ network that changes the categories of original DeepLabv3+ network is proposed to locate image regional forgery. In addition, the original images are added to the training dataset, and the ground truth of the original images are set to be black images, which will guide the network to learn the difference between the original images and the spliced images. Experimental results show that the improved DeepLabv3+ network has achieved a finer effect for locating image regional forgery than the existing algorithms on the CASIA database.

Key words: improved DeepLabv3+, image splicing, regional localization, contour learning, edge features

中图分类号:

TN929.53

张继威, 牛少彰, 曹志义, 王心怡. 基于改进DeepLabv3+的拼接篡改定位检测技术[J]. 北京邮电大学学报, 2019, 42(1): 68-73.

ZHANG Ji-wei, NIU Shao-zhang, CAO Zhi-yi, WANG Xin-yi. Locating Image Splicing by Improved DeepLabv3+[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(1): 68-73.

参考文献

[1] Zampoglou M, Papadopoulos S, Kompatsiaris Y. Large-scale evaluation of splicing localization algorithms for web images[J]. Multimedia Tools and Applications, 2017, 76(4):4801-4834.
[2] Liu Bo, Pun C M. Locating splicing forgery by fully convolutional networks and conditional random field[J]. Signal Processing:Image Communication, 2018, 66(4):103-112.
[3] Lyu Siwei, Pan Xunyu, Zhang Xing. Exposing region splicing forgeries with blind local noise estimation[J]. International Journal of Computer Vision, 2014, 110(2):202-221.
[4] Cozzolino D, Poggi G, Verdoliva L. Splicebuster:a new blind image splicing detector[C]//2015 IEEE International Workshop on Information Forensics and Security (WIFS). New York:IEEE, 2015:1-6.
[5] Binanchi T, Piva A. Detection of nonaligned double JPEG compression based on integer periodicity maps[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(2):842-848.
[6] Bianchi T, Piva A. Image forgery localization via block-grained analysis of JPEG artifacts[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(3):1003-1017.
[7] Amerini I, Becarelli R, Caldelli R, et al. Splicing forgeries localization through the use of first digit features[C]//2014 IEEE International Workshop on Information Forensics and Security (WIFS). New York:IEEE, 2014:143-148.
[8] Zhang Ying, Goh J, Win L L, et al. Image region forgery detection:a deep learning approach[C]//Proceedings of the Singapore Cyber-Security Conference (SG-CRC). Amsterdam:IOS, 2016:1-11.
[9] Bayar B, Stamm M C. A deep learning approach to universal image manipulation detection using a new convolutional layer[C]//Proceedings of the 4^th ACM Workshop on Information Hiding and Multimedia Security. New York:ACM, 2016:5-10.
[10] Bianchi T, Rosa A D, Piva A. Improved DCT coefficient analysis for forgery localization in JPEG images[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. New York:IEEE, 2011:2444-2447.
[11] Wang Wei, Dong Jing, Tan Tieniu. Exploring DCT coefficient quantization effects for local tampering detection[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(10):1653-1666.
[12] Chierchia G, Poggi G, Sansone C, et al. A Bayesian-MRF approach for PRNU-based image forgery detection[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(4):554-567.
[13] Pun C M, Liu Bo, Yuan Xiaochen. Multi-scale noise estimation for image splicing forgery detection[J]. Journal of Visual Communication and Image Representation, 2016, 38(3):195-206.
[14] 李叶舟, 孙晓婷, 牛少彰, 等. 噪声特征与EXIF信息相关性的图像篡改鉴定[J]. 北京邮电大学学报, 2014, 37(1):6-10. Li Yezhou, Sun Xiaoting, Niu Shaozhang, et al. Detecting forgeries by correlation between image noise features and EXIF parameters[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(1):6-10.
[15] Ferrara P, Bianchi T, Rosa A D, et al. Image forgery localization via fine-grained analysis of CFA artifacts[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(5):1566-1577.
[16] Chen Jiansheng, Kang Xiangui, Liu Ye, et al. Median filtering forensics based on convolutional neural networks[J]. IEEE Signal Processing Letters, 2015, 22(11):1849-1853.
[17] Bondi L, Lameri S, Guera D, et al. Tampering detection and localization through clustering of camera-based CNN features[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). New York:IEEE, 2017:1855-1864.
[18] Rao Yuan, Ni Jiangqun. A deep learning approach to detection of splicing and copy-move forgeries in images[C]//IEEE International Workshop on Information Forensics and Security. New York:IEEE, 2016:1-6.
[19] Salloum R, Ren Yuzhuo, Kuo C C J. Image splicing localization using a multi-task fully convolutional network (MFCN)[J]. Journal of Visual Communication and Image Representation, 2018, 51(2):201-209.
[20] Chen L C, Zhu Yukun, Papapndreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV). Berlin:Springer, 2018:801-818.

基于改进DeepLabv3+的拼接篡改定位检测技术

Locating Image Splicing by Improved DeepLabv3+

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