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We have proposed to use large representative training set and weighted ensemble classifiers to improve the classification robustness to high intra-class variation

Robust Steganalysis Based On Training Set Construction And Ensemble Classifiers Weighting

2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), pp.1498-1502, (2015)

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Abstract

The cover source mismatch problem in steganalysis is a serious problem which keeps current steganalysis from practical use. It is mainly because of the high intra-class variation of cover and stego samples in the feature space, since current steganalytic features are inevitably affected much by the image content, size, quality and many ot...More

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Introduction
  • Modern image steganalysis is usually considered as a supervised classification problem, in which images are represented by feature vectors and a classifier as steganalyzer is trained on cover and stego examples.
  • When the testing images are from a source very different from the training set, the performance often degrades significantly
  • This has been called the cover source mismatch problem in recent literature [1,2], and has been recognized as a serious issue that may complicate deployment of steganalyzers in real world.
  • The time and space complexity may be too high and the usefulness of some data is questionable
Highlights
  • Modern image steganalysis is usually considered as a supervised classification problem, in which images are represented by feature vectors and a classifier as steganalyzer is trained on cover and stego examples
  • The problem is that it is difficult to get a reliable division on a large data set with high diversity of samples. Another approach focuses on centering or normalizing samples in the feature space [1, 6], but the improvement is limited because the variation of samples is irregularly affected by so many factors
  • To construct a large representative training set, we propose using two criteria to select samples from a large amount of images on the Internet: 1) the distance from a sample to previous classifiers learned on the initial training set and, 2) the diversity of the selected samples
  • We have proposed to use large representative training set and weighted ensemble classifiers to improve the classification robustness to high intra-class variation
  • Experimental results show that our method can improve the performance and robustness of steganalysis under high intra-class variation
  • The proposed weighted ensemble classifiers, which are adaptive to the location of testing sample in the feature space, have been proved to be effective in the experiments
Methods
  • The authors' overall goal is to construct a training set with samples of high variation and low redundancy.
  • In the construction of training set, the authors try to select diverse and representative samples based on the idea of active learning and instance selection [13].
  • To make the final constructed training set more compact, the authors employ a diversity measure to further reduce the redundancy of the selected samples
Results
  • Experimental results show that the method can improve the performance and robustness of steganalysis under high intra-class variation.
  • By enlarging BOSSbase using image processing operations, the detection errors decrease quickly by more than 10%
Conclusion
  • The steganalyzer needs to be robust to various images from different sources.
  • The authors have proposed to use large representative training set and weighted ensemble classifiers to improve the classification robustness to high intra-class variation.
  • The proposed weighted ensemble classifiers, which are adaptive to the location of testing sample in the feature space, have been proved to be effective in the experiments.
  • The authors will do more experiments on different steganographic algorithms using massive image data.
  • The impact of the original database, the setting of the parameters λ, T, N , as well as the treatment for “unsure” samples will be further studied
Tables
  • Table1: Detection errors of classifiers on nsF5 0.2 bpnc. Feature space: LIU (216)
  • Table2: Detection errors of classifiers on nsF5 0.2 bpnc. Feature space: CC-PEV (548)
Download tables as Excel
Funding
  • This work is funded by the National Basic Research Program of China (Grant No 2012CB316300), the National Nature Science Foundation of China (Grant No.61303262), and the National Key Technology R&D Program (Grant No.2012BAH04F02)
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