J
Jishen Zeng
Researcher at Shenzhen University
Publications - 12
Citations - 358
Jishen Zeng is an academic researcher from Shenzhen University. The author has contributed to research in topics: Steganalysis & Deep learning. The author has an hindex of 6, co-authored 10 publications receiving 260 citations.
Papers
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Journal ArticleDOI
Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework
TL;DR: A generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models is proposed, and it is demonstrated that the framework is insensitive to JPEG blocking artifact alterations, and the learned model can be easily transferred to a different attacking target and even a different data set.
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Large-scale JPEG steganalysis using hybrid deep-learning framework
TL;DR: Wang et al. as mentioned in this paper proposed a generic hybrid deep learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models, which involves two main stages: the first stage is hand-crafted, corresponding to the convolution phase and the quantization & truncation phase of the rich models.
Journal ArticleDOI
WISERNet: Wider Separate-Then-Reunion Network for Steganalysis of Color Images
TL;DR: Wang et al. as discussed by the authors proposed a wider separate-then-reunion network (WISERNet) for color image steganalysis, which adopts separate channel-wise convolution without summation instead.
Journal ArticleDOI
WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images
TL;DR: The experimental results show that the proposed wider separate-then-reunion network (WISERNet) for steganalysis of color images outperforms other state-of-the-art color image steganalytic models either hand crafted or learned using deep networks in the literature by a clear margin.
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A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis
TL;DR: The 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.