Learning and transferring representations for image steganalysis using convolutional neural network

The major challenge of machine learning based image steganalysis lies in obtaining powerful feature representations. Recently, Qian et al. have shown that Convolutional Neural Network (CNN) is effective for learning features automatically for steganalysis. In this paper, we follow up this new paradi...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 2752 - 2756
Main Authors Yinlong Qian, Jing Dong, Wei Wang, Tieniu Tan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Convolution
Convolutional Neural Network
deep learning
Feature extraction
Kernel
Machine learning
Neural networks
Payloads
Steganalysis
Training
transfer learning
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Summary:The major challenge of machine learning based image steganalysis lies in obtaining powerful feature representations. Recently, Qian et al. have shown that Convolutional Neural Network (CNN) is effective for learning features automatically for steganalysis. In this paper, we follow up this new paradigm in steganalysis, and propose a framework based on transfer learning to help the training of CNN for steganalysis, hence to achieve a better performance. We show that feature representations learned with a pre-trained CNN for detecting a steganographic algorithm with a high payload can be efficiently transferred to improve the learning of features for detecting the same steganographic algorithm with a low pay-load. By detecting representative WOW and S-UNIWARD steganographic algorithms, we demonstrate that the proposed scheme is effective in improving the feature learning in CNN models for steganalysis.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532860