Forgery Detection of Digital Images Using Teaching–Learning Based Optimization and Principal Component Analysis-Reference-Cited by-同舟云学术

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Forgery Detection of Digital Images Using Teaching–Learning Based Optimization and Principal Component Analysis

Published:2023-06-14 Issue:1 Volume:24 Page:
ISSN:1557-2072
Container-title:Sensing and Imaging
language:en
Short-container-title:Sens Imaging

Author:

Uma S.,Sathya P. D.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Instrumentation

Link

https://link.springer.com/content/pdf/10.1007/s11220-023-00424-7.pdf

Reference24 articles.

1. Wang, Y., & Kamata, S. (2018). Copy move image forgery detection based on polar Fourier representation. International Journal of Machine Learning and Computing, 8(2), 158–163. https://doi.org/10.18178/ijmlc.2018.8.2.680

2. Zhang, Z., Wang, C., & Zhou, X. (2018). A survey on passive image copy-move forgery detection. Journal of Information Processing Systems, 14(1), 6–31. https://doi.org/10.3745/JIPS.02.0078

3. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. Int J Computer Vision, 60(2), 91–110.

4. Niu, P., Wang, C., Chen, W., Yang, H., & Wang, X. (2021). Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants. Journal of Visual Communication and Image Representation, 77, 1–11. https://doi.org/10.1016/j.jvcir.2021.103068

5. Rao, R., Savsani, V., & Vakharia, D. (2012). Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1–15. https://doi.org/10.1016/j.ins.2011.08.006

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