logo

SCIENCE CHINA Information Sciences, Volume 63, Issue 3: 139109(2020) https://doi.org/10.1007/s11432-018-9492-6

Image processing operations identification via convolutional neural network

More info
  • ReceivedMar 29, 2018
  • AcceptedJun 15, 2018
  • PublishedFeb 10, 2020

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61672551, 61602318), Special Research Plan of Guangdong Province (Grant No. 2015TQ01X365), Guangzhou Science and Technology Plan Project (Grant No. 201707010167), Shenzhen R${\rm~\&}$D Program (Grant No. JCYJ20160328144421330), and Alibaba Group through Alibaba Innovative Research Program.


Supplement

Figure A1, Tables A1–A6, Appendix B.


References

[1] Li H, Luo W, Qiu X. Identification of Various Image Operations Using Residual-Based Features. IEEE Trans Circuits Syst Video Technol, 2018, 28: 31-45 CrossRef Google Scholar

[2] Chen J, Kang X, Liu Y. Median Filtering Forensics Based on Convolutional Neural Networks. IEEE Signal Process Lett, 2015, 22: 1849-1853 CrossRef ADS Google Scholar

[3] Bayar B, Stamm M C. A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of ACM Workshop on Information Hiding and Multimedia Security, Vigo, 2016. 5--10. Google Scholar

[4] Xu G, Wu H Z, Shi Y Q. Structural Design of Convolutional Neural Networks for Steganalysis. IEEE Signal Process Lett, 2016, 23: 708-712 CrossRef ADS Google Scholar

[5] Bas P, Filler T, Pevn`y T. “Break our steganographic system": the ins and outs of organizing BOSS. Springer Information Hiding, 2011, 6958: 59-70. Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1