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A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis
( Israr Hussain ),( Jishen Zeng ),( Xinhong ),( Shunquan Tan ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.3
Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.
Ensemble Deep Learning Features for Real-World Image Steganalysis
( Ziling Zhou ),( Shunquan Tan ),( Jishen Zeng ),( Han Chen ),( Shaobin Hong ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.11
The Alaska competition provides an opportunity to study the practical problems of real-world steganalysis. Participants are required to solve steganalysis involving various embedding schemes, inconsistency JPEG Quality Factor and various processing pipelines. In this paper, we propose a method to ensemble multiple deep learning steganalyzers. We select SRNet and RESDET as our base models. Then we design a three-layers model ensemble network to fuse these base models and output the final prediction. By separating the three colors channels for base model training and feature replacement strategy instead of simply merging features, the performance of the model ensemble is greatly improved. The proposed method won second place in the Alaska 1 competition in the end.