TENet: leveraging transformer encoders for steganalysis of QIM steganography in VoIP speech streams
Author:
Funder
National Natural Science Foundation of China
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/s11042-023-17802-8.pdf
Reference46 articles.
1. Cogranne R, Giboulot Q, Bas P (2022) Efficient steganography in jpeg images by minimizing performance of optimal detector. IEEE Trans Inf Forensics Secur 17:1328–1343. https://doi.org/10.1109/TIFS.2021.3111713
2. Giboulot Q, Bas P, Cogranne R (2022) Multivariate side-informed gaussian embedding minimizing statistical detectability. IEEE Trans Inf Forensics Secur 17:1841–1854. https://doi.org/10.1109/TIFS.20223173184
3. Yi X et al (2019) Ahcm: Adaptive huffman code mapping for audio steganography based on psychoacoustic model. IEEE Trans Inf Forensics Secur 14(8):2217–2231. https://doi.org/10.1109/TIFS.2019.2895200
4. Chen K et al (2020) Derivative-based steganographic distortion and its non-additive extensions for audio. IEEE Trans Circ Syst Video Technol 30(7):2027–2032. https://doi.org/10.1109/TCSVT.2019.2918511
5. Yang Z et al (2019) Rnn-stega: Linguistic steganography based on recurrent neural networks. IEEE Trans Inf Forensics Secur 14(5):1280–1295. https://doi.org/10.1109/TIFS.2018.2871746
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