Image operator forensics and sequence estimation using robust deep neural network
Author:
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/s11042-023-17389-0.pdf
Reference36 articles.
1. Yang P, Baracchi D, Ni R et al (2020) A survey of deep learning-based source image forensics. J Imaging 6:9. https://doi.org/10.3390/jimaging6030009
2. Agarwal S, Jung K-H (2022) Median filtering forensics based on optimum thresholding for low-resolution compressed images. Multimed Tools Appl 81:7047–7062. https://doi.org/10.1007/s11042-022-11945-w
3. Peng L, Liao X, Chen M (2021) Resampling parameter estimation via dual-filtering based convolutional neural network. Multimed Syst 27:363–370. https://doi.org/10.1007/s00530-020-00697-y
4. Qiu X, Li H, Luo W, Huang J (2014) A universal image forensic strategy based on steganalytic model. In: IH and MMSec 2014 - Proceedings of the 2014 ACM Information Hiding and Multimedia Security Workshop. ACM Press, New York, New York, USA, pp 165–170
5. Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7:868–882. https://doi.org/10.1109/TIFS.2012.2190402
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