Effective steganalysis based on statistical moments of wavelet characteristic function
- 1 January 2005
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 768-773 Vol. 1
- https://doi.org/10.1109/itcc.2005.138
Abstract
In this paper, an effective steganalysis based on statistical moments of wavelet characteristic function is proposed. It decomposes the test image using two-level Haar wavelet transform into nine subbands (here the image itself is considered as the LL/sub 0/ subband). For each subband, the characteristic function is calculated. The first and second statistical moments of the characteristic functions from all the subbands are selected to form an 18-dimensional feature vector for steganalysis. The Bayes classifier is utilized in classification. All of the 1096 images from the CorelDraw image database are used in our extensive experimental work. With randomly selected 100 images for training and the remaining 996 images for testing, the proposed steganalysis system can steadily achieve a correct classification rate of 79% for the non-blind Spread Spectrum watermarking algorithm proposed by Cox et ai, 88% for the blind Spread Spectrum watermarking algorithm proposed by Piva et ai, and 91% for a generic LSB embedding method, thus indicating significant advancement in steganalysis.This publication has 4 references indexed in Scilit:
- Detecting LSB steganography in color, and gray-scale imagesIEEE MultiMedia, 2001
- Secure spread spectrum watermarking for multimediaIEEE Transactions on Image Processing, 1997
- DCT-based watermark recovering without resorting to the uncorrupted original imagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Bhattacharyya distance feature selectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996