Payload Estimation in Universal Steganalysis (Short Communication)

Amritha P.P.; Anoj Madathil

doi:10.14429/dsj.60.499

Amritha P.P. Amrita Vishwa Vidyapeetham, Coimbatore
Anoj Madathil Amrita Vishwa Vidyapeetham University, Coimbatore

DOI: https://doi.org/10.14429/dsj.60.499

Keywords: Support vector machine, support vector regression, principal component analysis, steganalysis, payload

Abstract

Universal Steganalysis can classify images without the knowledge of steganographic algorithms. This steganalysis will blindly classify an image as cover or not, but finding how much payload embedded, is still an open problem. This paper focuses on the above problem. Firstly, they use features from universal steganlysers and apply principal component analysis to improve the false positive rate. The above features are then used to estimate the payload by using support vector regression. The support vector machine classifier capable of assigning stego images to six popular steganographic algorithm after applying Principal Component Analysis: JP Hide & Seek, PVD, LSB flipping, Outguess, S-Tool and F5 is trained. This provides significantly more reliable results compared to their previous work on universal steganalysis. The performance is also evaluated by quantitative steganalysis for six steganographic algorithms.

Defence Science Journal, 2010, 60(4), pp.412-414, DOI:http://dx.doi.org/10.14429/dsj.60.499

Author Biographies

Amritha P.P., Amrita Vishwa Vidyapeetham, Coimbatore

Research Associate at TIFAC CORE in Cyber Security, Amrita Vishwa Vidyapeetham University, Coimbatore. She received her MTech (Cyber Security) from Amrita Vishwa Vidyapeetham in 2009. She is pursuing PhD in Multimedia Security.

Anoj Madathil, Amrita Vishwa Vidyapeetham University, Coimbatore

Junior Research Fellow at TIFAC CORE in Cyber Security, Amrita Vishwa Vidyapeetham University, Coimbatore. He received MCA from University of Calicut in 2009. He is pursuing MS (by Research) at Amrita Vishwa Vidyapeetham.