Image Forgery Detection & Localization Using Regularized U-Net

Author:

Qureshi Mohammed MurtuzaORCID,Qureshi Mohammed GhalibORCID

Publisher

Springer Singapore

Reference13 articles.

1. Qazi, T., et al.: Survey on blind image forgery detection. IET Image Proc. 7(7), 660–670 (2013)

2. Lecture Notes in Computer Science;M Huh,2018

3. Wagner, J.: Error Level Analysis. FotoForensics (2012). https://fotoforensics.com/tutorial-ela.php

4. Sudiatmika, I.B., Rahman, F.J., Trisno, T., Suyoto, S.: Image forgery detection using error level analysis and deep learning. TELKOMNIKA Telecommun. Comput. Electron. Control 17, 653–659 (2018)

5. Bunk, J., et al.: Detection and localization of image forgeries using resampling features and deep learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1881–1889. IEEE, July 2017

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1. Deep Localization on Mixed Image Tempering Techniques Using U-Net;2024 IEEE Students Conference on Engineering and Systems (SCES);2024-06-21

2. Enhancing Digital Image Forgery Detection Using Transfer Learning;IEEE Access;2023

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