Authors
Soumik Mondal, Yeo Sze Ling, Arulmurugan Ambikapathi
Publication date
2021/7/5
Conference
2021 IEEE International Conference on Multimedia and Expo (ICME)
Pages
1-6
Publisher
IEEE
Description
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 …
Total citations
20212022202320241221
Scholar articles
S Mondal, YS Ling, A Ambikapathi - 2021 IEEE International Conference on Multimedia and …, 2021