An Analytical Review of Machine Learning and Deep Learning Approaches for Document Forgery Detection-Reference-Cited by-同舟云学术

An Analytical Review of Machine Learning and Deep Learning Approaches for Document Forgery Detection

Published:2022 Issue: Volume: Page:629-638
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Jaiswal Garima,Sharma Arun,Yadav Sumit Kumar

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-2828-4_56

Reference30 articles.

1. Srivastava S, Rastogi V, Jaiswal G, Sharma A (2022) Hyperspectral imaging in document forgery. in proceedings of data analytics and management, pp 105–115. Springer, Singapore

2. Jaiswal G, Sharma A, Yadav SK Critical insights into modern hyperspectral image applications through deep learning. Wiley interdisciplinary reviews: data mining and knowledge discovery, e1426

3. Bibi M, Hamid A, Moetesum M, Siddiqi I (2019) Document forgery detection using printer source identification—a text-independent approach. In: 2019 international conference on document analysis and recognition workshops (ICDARW). IEEE vol 8, pp 7–12

4. Kim DG, Hou JU, Lee HK (2019) Learning deep features for source color laser printer identification based on cascaded learning. Neurocomputing 365:219–228

5. Tsai MJ, Yuadi I, Tao YH (2018) Decision-theoretic model to identify printed sources. Multimedia Tools Appl 77(20):27543–27587

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