CNN-Based Models for Image Forgery Detection

Author:

Singh Rajput Shyam,Rai Deepak,Hothrik Deeti,Kumar Sudhanshu,Singh Shubhangi

Publisher

Springer Nature Singapore

Reference11 articles.

1. Rao, Y., Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. 978-1-5090-1138-4/16/

2. Barad, Z.J., Goswami, M.M.: Image forgery detection using deep learning: a survey. 978-1-7281-5197-7/20/

3. Zhang, Y., Goh, J., Win, L.L., Thing, V.L.: Image region forgery detection: a deep learning approach. In: SG-CRC, pp. 1–11 (2016)

4. Chen, J., Kang, X., Liu, Y., Wang, Z.J.: Median filtering forensics based on convolutional neural networks. IEEE Signal Process. Lett. 22(11), 1849–1853 (2015)

5. Bondi, S., Lameri, D., Güera, P., Bestagini, E., Delp, J., Tubaro, S.: Tampering detection and localization through clustering of camera-based cnn features. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp. 1855–1864. IEEE (2017)

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