Fake region identification in an image using deep learning segmentation model-Reference-Cited by-同舟云学术

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Fake region identification in an image using deep learning segmentation model

Published:2023-03-27 Issue:25 Volume:82 Page:38901-38921
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Jaiswal Ankit Kumar,Srivastava Rajeev

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-15032-6.pdf

Reference49 articles.

1. Agarwal S, Chand S (2015) Image forgery detection using multi scale entropy filter and local phase quantization. Int J Image Graph Signal Process 7 (10):78

2. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

3. Bappy JH, Simons C, Nataraj L, Manjunath B, Roy-Chowdhury AK (2019) Hybrid lstm and encoder–decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286–3300

4. Bianchi T, De Rosa A, Piva A (2011) Improved dct coefficient analysis for forgery localization in jpeg images. In: 2011 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2444–2447

5. Chen B, Tan W, Coatrieux G, Zheng Y, Shi YQ (2020) A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE transactions on multimedia

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