Analysis of image forgery detection using convolutional neural network

被引：0
作者：
Gnaneshwar C. [1 ]
Singh M.K. [1 ]
Yadav S.S. [1 ]
Balabantaray B.K. [2 ]
机构：
[1] Department of Electronics and Communication Engineering, NIT Meghalaya, Shillong
[2] Department of Computer Science and Engineering, NIT Meghalaya, Shillong
来源：
International Journal of Applied Systemic Studies | 2022年 / 9卷 / 03期
关键词：
CNN; convolutional neural network; deep learning; DL; ELA; error level analysis; image forgery detection; machine learning; ML;
D O I：
10.1504/IJASS.2022.124085
中图分类号：
学科分类号：
摘要：
Prior to the age of cameras, if someone wanted to see/verify any incident or document, then one must go to that place and verify. The fact is that no one ever questions once someone has verified something with their own eyes. Nowadays, with the rapid development of new technologies, one cannot be sure of an image, which one is a copy of the sight or not a sight itself. Such types of verifications are not possible in the current time due to the development of varieties of advanced image editing tools like Corel draw, Photoshop, GIMP, etc. These are low cost and open-source tools for the users and frequently used to make memes on social media websites. This paper presents an image forgery detection using convolutional neural networks (CNNs/ConvNet). The error level analysis (ELA) method is discussed in detail for image forgery detection. The binary decision of CNN-based model helps in declaration of an image aptness for official uses. The CNN model has been trained for the Kaggle dataset and detailed simulations have been carried out to validate the accuracy and precision of the proposed model. Copyright © 2022 Inderscience Enterprises Ltd.
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页码：240 / 260
页数：20
相关论文
共 31 条
  • [1] Ansari M.D., Ghrera S.P., Tyagi V., Pixel-based image forgery detection: a review, IETE Journal of Education, 55, 1, pp. 40-46, (2014)
  • [2] Bayar B., Stamm M.C., A deep learning approach to universal image manipulation detection using a new convolutional layer, Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, (2016)
  • [3] Bharti C.N., Tandel P., A survey of image forgery detection techniques, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), (2016)
  • [4] Birajdar G.K., Mankar V.H., Digital image forgery detection using passive techniques: a survey, Digital Investigation, 10, 3, pp. 226-245, (2013)
  • [5] Chen J., Kang X., Liu Y., Wang Z.J., Median filtering forensics based on convolutional neural networks, IEEE Signal Processing Letters, 22, 11, pp. 1849-1853, (2015)
  • [6] CS231n Convolutional Neural Networks for Visual Recognition
  • [7] Das D., Nayak D.R., Dash R., Majhi B., Zhang Y-D., H-WordNet: a holistic convolutional neural network approach for handwritten word recognition, IET Image Processing, 14, 9, pp. 1794-1805, (2020)
  • [8] Doegar A., Dutta M., Gaurav K., CNN based image forgery detection using pre-trained AlexNet model, International Journal of Computational Intelligence & IoT, 2, 1, pp. 1-6, (2019)
  • [9] Dureja A., Pahwa P., Image retrieval techniques: a survey, International Journal of Engineering & Technology, 7, 1–2, pp. 215-219, (2018)
  • [10] Farid H., Image forgery detection, IEEE Signal Processing Magazine, 26, 2, pp. 16-25, (2009)
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