收稿日期: 2018-02-06
网络出版日期: 2018-03-31
基金资助
国家自然科学基金（No.61232016，No.61502242）资助
Local Blur Detection of Digital Images Based on Deep Learning
Received date: 2018-02-06
Online published: 2018-03-31
杨滨, 张涛, 陈先意 . 基于深度学习的图像局部模糊识别[J]. 应用科学学报, 2018 , 36(2) : 321 -330 . DOI: 10.3969/j.issn.0255-8297.2018.02.011
Blurring is generally a post-operation to conceal or remove the trace of tampering. In this paper, a new convolutional neural network model is proposed, and the corresponding network topology is presented to handle the problems in the detection of blur operations, such as Gaussian blur, average blur, or median blur. An information process layer is added into the conventional convolutional neural network to extract the residual features of fltering frequency domain, accordingly, improving the accuracy of blur detection between the frst-order and the second-order fltering operations. Experimental results demonstrate that the proposed method performs a higher accuracy in blur detection than traditional methods, and is able to discriminate between the common linear and nonlinear blur operations.
[1] Yang B, Sun X M, Guo H L, Xia Z H, Chen X Y. A copy-move forgery detection method based on CMFD-SIFT[J]. Multimedia Tools and Applications, 2018, 77(1):837-855.
[2] Kirchner M, Fridrich J. On detection of median fltering in digital images[J]. Media Forensics & Security Ⅱ, 2010, 7541(1):754110.
[3] 彭安杰,康显桂. 基于滤波残差多方向差分的中值滤波取证技术[J]. 计算机学报,2016, 39(3):503-515. Peng A J, Kang X G. Median fltering forensics based on multi-directional difference of fltering residuals[J]. Chinese Journal of Computers, 2016, 39(3):503-515. (in Chinese)
[4] 李明富,王晨,彭安杰,陈立伟. 数字图像模糊滤波盲取证算法[J]. 半导体光电,2017, 38(3):430-434. Li M F, Wang C, Peng A J, Chen L W. A blind forensics algorithm for digital image smoothing fltering[J]. Semiconductor Optoelectronics, 2017, 38(3):430-434. (in Chinese)
[5] Dixit R, Naskar R. Review, analysis and parameterization of techniques for copy-move forgery detection in digital images[J]. IET Image Processing, 2017, 11(9):746-759.
[6] Chen J, Kang X, Liu Y, Wang Z J. Median fltering forensics based on convolutional neural networks[J]. IEEE Signal Processing Letters, 2015, 22(11):1849-1853.
[7] Gu B, Sun X, Sheng V S. Structural minimax probability machine[J]. IEEE Transactions on Neural Networks & Learning Systems, 2017, 28(7):1646-1656.
[8] Bayar B, Stamm M C. A deep learning approach to universal image manipulation detection using a new convolutional layer[J]. ACM Workshop on Information Hiding and Multimedia Security, 2016:5-10.
[9] Liu A, Zhao Z, Zhang C, Su Y. Smooth fltering identifcation based on convolutional neural networks[J]. Multimedia Tools & Applications, 2016:1-15.
[10] 周飞燕,金林鹏,董军. 卷积神经网络研究综述[J]. 计算机学报,2017, 40(6):1229-1251. Zhou F Y, Jin L P, Dong J. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6):1229-1251. (in Chinese)
[11] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout:a simple way to prevent neural networks from overftting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[12] Bas P, Filler T, Pevný T. "Break our steganographic system":the ins and outs of organizing BOSS[J]. Journal of the American Statistical Association, 2011, 96(454):488-499.
[13] Schaefer G. UCID:an uncompressed color image database[C]//Storage & Retrieval Methods & Applications for Multimedia. DBLP, 2003:472-480.
[14] Dong J, Wang W, Tan T. CASIA image tampering detection evaluation database[C]//IEEE China Summit & International Conference on Signal and Information Processing, 2013:422-426.
[15] Xu J, Ling Y, Zheng X. Forensic detection of Gaussian low-pass fltering in digital images[C]//International Congress on Image and Signal Processing, 2015:819-823.
/
| 〈 |
|
〉 |
