Image Forgery Detection System Using Convolution Neural Networks

Author:

Sreenivasulu G.,Sujatha B.,Venu Madhav K.,Rajalakshmi Selvaraj,Thulasi Chitra N.,Lingaswamy B.

Publisher

Springer Nature Singapore

Reference14 articles.

1. Sreenivasulu G et al (2011) Our-NIR: modified node importance representative for clustering of categorical data. Int J Comput Sci Inf Secur (IJCSIS) 9(4):146–150. ISSN 1947-5500

2. Marra F, Poggi G, Sansone C, Verdoliva L (2015) Evaluation of residual-based local features for camera model identification. In: International conference on image analysis and processing. Springer, pp 11–18

3. Sreenivsulu G, Viswanadha Raju S et al (2011) A threshold for clusting concept—drifting categorical data. In: IEEE 3rd International conference on machine learning and computing (ICMLC), vol 3, pp 383–387. ISBN: 978-1-4244-9253/11/$26,00@2011 IEEE

4. Tuama A, Comby F, Chaumont M (2016) Camera model identification with the use of deep convolutional neural networks. In: 2016 IEEE International workshop on information forensics and security (WIFS). IEEE, pp 1–6

5. Barni M, Bondi L, Bonettini N, Bestagini P, Costanzo A, Maggini M, Tondi B, Tubaro S (2017) Aligned and non-aligned double jpeg detection using convolutional neural networks. J Visual Commun Image Representation 49:153–163

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