Paper Info
Title
Quantitative and Binary Steganalysis in JPEG - A Comparative Study.
Abstract
We consider the problem of steganalysis, in which Eve (the steganalyst) aims to identify a steganographer, Alice who sends images through a network. We can also hypothesise that Eve does not know how many bits Alice embed in an image. In this paper, we investigate two different steganalysis scenarios: Binary Steganalysis and Quantitative Steganalysis. We compare two classical steganalysis algorithms from the state-of-the-art: the QS algorithm and the GLRT-Ensemble Classifier, with features extracted from JPEG images obtained from BOSSbase 1.01. As their outputs are different, we propose a methodology to compare them. Numerical results with a state-of-the-art Content Adaptive Embedding Scheme and a Rich Model show that the approach of the GLRT-ensemble is better than the QS approach when doing Binary Steganalysis but worse when doing Quantitative Steganalysis.
Year
DOI
Venue
2018
10.23919/EUSIPCO.2018.8553580
European Signal Processing Conference
Keywords
Field
DocType
Steganography,Quantitative Steganalysis,Binary Steganalysis,Multi-class Steganalysis,JPEG
Steganography,Embedding,Pattern recognition,Computer science,Transform coding,Feature extraction,JPEG,Artificial intelligence,Steganalysis,Classifier (linguistics),Binary number
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Ahmad Zakaria100.34
Marc Chaumont217220.40
Gérard Subsol339384.30