Ensemble Deep Learning Features for Real-World Image Steganalysis

• Ziling Zhou,
• Shunquan Tan,
• Jishen Zeng,
• Han Chen,
• Shaobin Hong,

Vol. 14, No. 11, November 30, 2020
10.3837/tiis.2020.11.017, Download Paper (Free):

• Steganalysis
• Deep Learning
• Color JPEG images
• feature fusion
• Ensemble model

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.

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