Name
Abstract | ||
|---|---|---|
This paper introduces a new statistical model for blind steganalysis of JPEG images. The functionals used to build this model are based on Huffman Bit Code Lengths (HBCL statistics) and the file size to image resolution ratio (FR Index). JPEG images spanning a wide range of resolutions were used to create a ‘stego-image’ database employing three embedding schemes – the advanced Least Significant Bit encoding technique, JPEG Hide-and-Seek and Model Based Steganography. Existing blind steganalysis techniques mostly involve the analyses of generalized category attacks and the higher order statistics. This work builds an effective neural network prediction model using HBCL statistics and FR Index, which are not yet explored by steganalysts. The experimental results proved to be efficient over a diverse image database and several payloads. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13601-6_13 | PAISI |
Keywords | Field | DocType |
diverse image database,hbcl statistic,fr index,huffman bit,blind steganalysis,blind steganalysis technique,jpeg hide-and-seek,new statistical model,significant bit,jpeg image,prediction model,steganography,indexation,least significant bit,image resolution,neural networks,statistical model,neural network | Data mining,Computer science,Huffman coding,Artificial intelligence,Steganography,Pattern recognition,Higher-order statistics,File size,JPEG,Statistical model,Steganalysis,Statistics,Least significant bit | Conference |
Volume | ISSN | ISBN |
6122 | 0302-9743 | 3-642-13600-1 |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Veena H. Bhat | 1 | 5 | 2.50 |
| Krishna S. | 2 | 9 | 8.31 |
| P. Deepa Shenoy | 3 | 117 | 15.23 |
| Venugopal K. R. | 4 | 1 | 1.04 |
| L M Patnaik | 5 | 1617 | 198.06 |