JPEG steganalysis using HBCL statistics and FR index

Authors:
Veena H. Bhat;Krishna S.;P. Deepa Shenoy;Venugopal K. R.;L. M. Patnaik
Affiliations:
Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India;Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India;Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India;Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India;Vice Chancellor, Defense Institute of Advanced Technology, Pune, India
Venue:
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
Year:
2010

Citing 5
Cited 0

Attacks on Steganographic Systems

IH '99 Proceedings of the Third International Workshop on Information Hiding
Reliable detection of LSB steganography in color and grayscale images

MM&Sec '01 Proceedings of the 2001 workshop on Multimedia and security: new challenges
Steganography Detection by Means of Neural Networks

DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Multi-class Blind Steganalysis Based on Image Run-Length Analysis

IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
YASS: yet another steganographic scheme that resists blind steganalysis

IH'07 Proceedings of the 9th international conference on Information hiding

Quantified Score

Hi-index	0.00

Visualization

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.