H-Stegonet: A Hybrid Deep Learning Framework for Robust Steganalysis

H-Stegonet: A Hybrid Deep Learning Framework for Robust Steganalysis
Title:
H-Stegonet: A Hybrid Deep Learning Framework for Robust Steganalysis
Other Titles:
2021 IEEE International Conference on Multimedia and Expo (ICME)
Publication Date:
09 June 2021
Citation:
Mondal, S., Ling, Y. S., Ambikapathi, A. (2021). H-Stegonet: A Hybrid Deep Learning Framework for Robust Steganalysis. 2021 IEEE International Conference on Multimedia and Expo (ICME). doi:10.1109/icme51207.2021.9428374
Abstract:
Steganalysis can be characterized as detecting a weak noise signal (hidden information) in textured regions of naturally occurring images. These noise signals are typically not perceptible to human eyes, which renders steganalysis a challenging task. On the other hand, recent breakthroughs in deep learning have seen remarkable progress in many applications, ranging from object recognition and segmentation to image generations. While there were efforts to build deep learning networks to perform steganalysis, the proposed architectures exhibit some limitations and a high tendency to overfit. We propose a hybrid deep learning architecture, namely H-StegoNet, to perform spatial steganalysis in this work. Precisely, by combining two different neural networks inspired by handcrafted features and the U-Net, we design a robust architecture that outperforms the existing approaches. Moreover, the experiments we performed under more realistic assumptions, including encoding with the syndrome trellis codes and assuming no prior knowledge of the payload used, thereby defining a rigorous and standard operation procedure for evaluating any steganalysis algorithm.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. : NA
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1945-788X
1945-7871
ISBN:
978-1-6654-3864-3
978-1-6654-1152-3
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