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http://dx.doi.org/10.3837/tiis.2020.11.017

Ensemble Deep Learning Features for Real-World Image Steganalysis  

Zhou, Ziling (College of Computer Science and Software Engineering, Shenzhen University)
Tan, Shunquan (College of Computer Science and Software Engineering, Shenzhen University)
Zeng, Jishen (College of Information Engineering, Shenzhen University)
Chen, Han (College of Information Engineering, Shenzhen University)
Hong, Shaobin (College of Information Engineering, Shenzhen University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.11, 2020 , pp. 4557-4572 More about this Journal
Abstract
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.
Keywords
Steganalysis; Deep learning; Color JPEG images; Feature fusion; Ensemble model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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