A new approach to video steganography models with 3D deep CNN autoencoders

Author:

Telli MounirORCID,Othmani Mohamed,Ltifi Hela

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference32 articles.

1. Abdolmohammadi M, Toroghi RM, Bastanfard A (2020) Video steganography using 3d convolutional neural networks. In: Djeddi C, Jamil A, Siddiqi I (eds) Pattern recognition and artificial intelligence. Springer International Publishing, Cham, pp 149–161. https://doi.org/10.1007/978-3-030-37548-5_12

2. Mohammed HA, Saffar NFH Al (2021) Lsb based image steganography using mceliece cryptosystem. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.07.182

3. Abood OG, Guirguis SK (2018) a survey on cryptography algorithms. Int J Sci Res Publ 8(7):495–516. https://doi.org/10.29322/IJSRP.8.7.2018.p7978

4. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Guyon I, Dror G, Lemaire V, et al (eds) Proceedings of ICML workshop on unsupervised and transfer learning, proceedings of machine learning research, vol 27. PMLR, Bellevue, Washington, USA, pp 37–49

5. Baluja S (2017) hiding images in plain sight: Deep steganography. In: Guyon I, Luxburg UV, Bengio S et al (eds) Advances in neural information processing systems, vol 30. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/file/838e8afb1ca34354ac209f53d90c3a43-Paper.pdf

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