Going from small to large data in steganalysis

  title={Going from small to large data in steganalysis},
  author={Ivans Lubenko and Andrew D. Ker},
  booktitle={Media Watermarking, Security, and Forensics},
With most image steganalysis traditionally based on supervised machine learning methods, the size of training data has remained static at up to 20000 training examples. This potentially leads to the classifier being undertrained for larger feature sets and it may be too narrowly focused on characteristics of a source of cover images, resulting in degradation in performance when the testing source is mismatched or heterogeneous. However it is not difficult to obtain larger training sets for… CONTINUE READING
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