Digital image forgery: taxonomy, techniques, and tools–a comprehensive study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-022-01829-5.pdf
Reference91 articles.
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3. Bahrami K, Kot AC, Li L, Li H (2015) Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans Inf Forensics Secur 10(5):999–1009
4. Barad ZJ, Goswami MM (2020) Image forgery detection using deep learning: a survey. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), pp. 571–576. IEEE
5. Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: European conference on computer vision, pp. 404–417. Springer, Berlin, Heidelberg
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1. Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis;Journal of Electronic Imaging;2024-07-04
2. Combating medical image tampering using deep transfer learning;AIP Conference Proceedings;2024
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