Hybrid convolutional neural network architecture driven by residual features for steganalysis of spatial steganographic algorithms-Reference-Cited by-同舟云学术

Hybrid convolutional neural network architecture driven by residual features for steganalysis of spatial steganographic algorithms

Published:2021-03-15 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Arivazhagan S.,Amrutha E.^ORCID,Sylvia Lilly Jebarani W.,Veena S. T.

Funder

Defence Research and Development Organisation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-021-05837-7.pdf

Reference51 articles.

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2. Fridrich J (2010) Steganography in digital media: principles, algorithms, and applications. Cambridge University Press, Cambridge, New York

3. Nissar A, Mir AH (2010) Classification of steganalysis techniques: a study. Digital Signal Process 20:1758–1770. https://doi.org/10.1016/j.dsp.2010.02.003

4. Fridrich J, Lisoněk P, Soukal D (2007) On Steganographic embedding efficiency. In: Camenisch JL, Collberg CS, Johnson NF, Sallee P (eds) Information Hiding. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 282–296

5. Arivazhagan S, Sylvia Lilly Jebarani W, Keerthinathan S (2015) Extended Adaptive Pixel Pair Matching for data hiding in medical images. Journal of Intelligent & Fuzzy Systems: 877–883. https://doi.org/https://doi.org/10.3233/IFS-151617

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